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![Scene Text Detection Resources(场景文字识别资源汇总) [转载] [翻译]](https://npm.elemecdn.com/typecho-joe-latest/assets/img/lazyload.jpg)
Scene Text Detection Resources(场景文字识别资源汇总) [转载] [翻译] 1. 数据集1.1 水平文字数据集ICDAR 2003(IC03)：Introduction: 它总共包含509张图像，258张用于训练和251张用于测试。 具体来说，它在训练集中包含1110个文本实例，而在测试集中包含1156个文本实例。 它具有单词级注释。 IC03仅考虑英文文本实例。Link: IC03-downloadICDAR 2011(IC11):Introduction: IC11是用于文本检测的英语数据集。 它包含484张图像，229张用于训练和255张用于测试。 该数据集中有1564个文本实例。 它提供单词级和字符级注释。Link:11-downloadICDAR 2013(IC13)：Introduction: IC13与IC11几乎相同。 它总共包含462张图像，用于训练的229张图像和用于测试的233张图像。 具体来说，它在训练集中包含849个文本实例，而在测试集中包含1095个文本实例。Link: IC13-download1.2 任意四边形文本数据集USTB-SV1K：Introduction：USTB-SV1K是英语数据集。 它包含来自Google街景视图的1000张街道图像，总共2955个文本实例。 它仅提供单词级注释。Link: USTB-SV1K-downloadSVT：Introduction:它包含350张图像，总共725个英文文本实例。 SVT具有字符级别和单词级别的注释。 DVT的图像是从Google街景视图中获取的，分辨率较低。Link: SVT-downloadSVT-P：Introduction: 它包含639个裁剪的单词图像以进行测试。 从Google街景视图的侧面快照中选择了图像。 因此，大多数图像会因非正面视角而严重失真。 它是SVT的改进数据集。Link: SVT-P-download (Password : vnis)ICDAR 2015(IC15)：Introduction: 它总共包含1500张图像，1000张用于训练和500张用于测试。 具体来说，它包含17548个文本实例。 它提供单词级别的注释。 IC15是第一个附带场景文本数据集，并且仅考虑英语单词。Link: IC15-downloadCOCO-Text：Introduction: 它总共包含63686张图像，用于训练的43686张图像，用于验证的10000张图像和用于测试的10000张图像。 具体来说，它包含145859个裁剪的单词图像以进行测试，包括手写和打印，清晰和模糊，英语和非英语。Link: COCO-Text-downloadMSRA-TD500：Introduction: 它总共包含500张图像。 它提供文本行级别的注释而不是单词，并提供多边形框而不是轴对齐的矩形来进行文本区域注释。 它包含英文和中文文本实例。Link: MSRA-TD500-downloadMLT 2017：Introduction:它总共包含10000个自然图像。 它提供单词级别的注释。 MLT有9种语言。 它是用于场景文本检测和识别的更真实和复杂的数据集。Link: MLT-downloadMLT 2019:Introduction: 它总共包含18000张图像。 它提供单词级别的注释。 与MLT相比，此数据集有10种语言。 它是用于场景文本检测和识别的更真实和复杂的数据集。Link: MLT-2019-downloadCTW：Introduction:它包含32285个中文文本的高分辨率街景图像，总共包含1018402个字符实例。 所有图像都在字符级别进行注释，包括其基础字符类型，绑定框和其他6个属性。 这些属性指示其背景是否复杂，是否凸起，是否为手写或印刷，是否被遮挡，是否扭曲，是否使用艺术字。Link: CTW-downloadRCTW-17：Introduction:它总共包含12514张图像，用于训练的11514张图像和用于测试的1000张图像。 RCTW-17中的图像大部分是通过照相机或手机收集的，其他则是生成的图像。 文本实例用平行四边形注释。 它是第一个大规模的中文数据集，也是当时发布的最大的数据集。Link: RCTW-17-downloadReCTS：Introduction:该数据集是大规模的中国街景商标数据集。 它基于中文单词和中文文本行级标签。 标记方法是任意四边形标记。 它总共包含20000张图像。Link: ReCTS-download1.3 不规则文本数据集CUTE80：Introduction: 它包含在自然场景中拍摄的80张高分辨率图像。 具体来说，它包含288个裁剪的单词图像以进行测试。 数据集集中在弯曲的文本上。 没有提供词典。Link: CUTE80-downloadTotal-Text：Introduction: 它总共包含1,555张图像。 具体来说，它包含11459个经裁剪的单词图像，这些图像具有三种以上不同的文本方向：水平，多方向和弯曲。Link: Total-Text-downloadSCUT-CTW1500：Introduction: 它总共包含1500张图像，1000张用于训练和500张用于测试。 具体来说，它包含10751个裁剪的单词图像以进行测试。 CTW-1500中的注释是具有14个顶点的多边形。 数据集主要由中文和英文组成。Link: CTW-1500-downloadLSVT：Introduction: LSVT由20,000个测试数据，30,000个完整注释的训练数据和400,000个弱注释的训练数据组成，这些数据称为部分标签。 带标签的文本区域展示了文本的多样性：水平，多向和弯曲。Link: LSVT-downloadArTs：Introduction: ArT包含10,166张图像，5,603张用于训练和4,563张用于测试。 收集它们时会考虑到文本形状的多样性，并且所有文本形状在ArT中都有大量存在。Link: ArT-download1.4 合成数据集Synth80k :Introduction:它包含80万幅图像，其中包含约800万个合成词实例。 每个文本实例都用其文本字符串，单词级和字符级的边界框进行注释。Link: Synth80k-downloadSynthText :Introduction:它包含600万个裁剪的单词图像。 生成过程与Synth90k相似。 它也以水平样式进行注释。Link: SynthText-download1.5 数据集对比 Comparison of Datasets Datasets Language Image Text instance Text Shape Annotation level Total Train Test Total Train Test Horizontal Arbitrary-Quadrilateral Multi-oriented Char Word Text-Line IC03 English 509 258 251 2266 1110 1156 ✓ ✕ ✕ ✕ ✓ ✕ IC11 English 484 229 255 1564 ～ ～ ✓ ✕ ✕ ✓ ✓ ✕ IC13 English 462 229 233 1944 849 1095 ✓ ✕ ✕ ✓ ✓ ✕ USTB-SV1K English 1000 500 500 2955 ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ SVT English 350 100 250 725 211 514 ✓ ✓ ✕ ✓ ✓ ✕ SVT-P English 238 ～ ～ 639 ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ IC15 English 1500 1000 500 17548 122318 5230 ✓ ✓ ✕ ✕ ✓ ✕ COCO-Text English 63686 43686 20000 145859 118309 27550 ✓ ✓ ✕ ✕ ✓ ✕ MSRA-TD500 English/Chinese 500 300 200 ～ ～ ～ ✓ ✓ ✕ ✕ ✕ ✓ MLT 2017 Multi-lingual 18000 7200 10800 ～ ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ MLT 2019 Multi-lingual 20000 10000 10000 ～ ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ CTW Chinese 32285 25887 6398 1018402 812872 205530 ✓ ✓ ✕ ✓ ✓ ✕ RCTW-17 English/Chinese 12514 15114 1000 ～ ～ ～ ✓ ✓ ✕ ✕ ✕ ✓ ReCTS Chinese 20000 ～ ～ ～ ～ ～ ✓ ✓ ✕ ✓ ✓ ✕ CUTE80 English 80 ～ ～ ～ ～ ～ ✕ ✕ ✓ ✕ ✓ ✓ Total-Text English 1525 1225 300 9330 ～ ～ ✓ ✓ ✓ ✕ ✓ ✓ CTW-1500 English/Chinese 1500 1000 500 10751 ～ ～ ✓ ✓ ✓ ✕ ✓ ✓ LSVT English/Chinese 450000 430000 20000 ～ ～ ～ ✓ ✓ ✓ ✕ ✓ ✓ ArT English/Chinese 10166 5603 4563 ～ ～ ～ ✓ ✓ ✓ ✕ ✓ ✕ Synth80k English 80k ～ ～ 8m ～ ～ ✓ ✕ ✕ ✓ ✓ ✕ SynthText English 800k ～ ～ 6m ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ 2. 场景文本检测资源总结2.1 方法对比场景文本检测方法可以分为四个部分：(a) 传统方法; (b) 基于分割的方法;(c) 基于回归的方法;(d) 混合方法.注意：（1）“ Hori”代表水平场景文本数据集。 （2）“ Quad”代表任意四边形文本数据集。（3）“ Irreg”代表不规则场景文本数据集。 （4）“传统方法”代表不依赖深度学习的方法。2.1.1 传统方法 Method Model Code Hori Quad Irreg Source Time Highlight Yao et al. [1] TD-Mixture ✕ ✓ ✓ ✕ CVPR 2012 1) A new dataset MSRA-TD500 and protocol for evaluation. 2) Equipped a two-level classification scheme and two sets of features extractor. Yin et al. [2] ✕ ✓ ✕ ✕ TPAMI 2013 Extract Maximally Stable Extremal Regions (MSERs) as character candidates and group them together. Le et al. [5] HOCC ✕ ✓ ✓ ✕ CVPR 2014 HOCC + MSERs Yin et al. [7] ✕ ✓ ✓ ✕ TPAMI 2015 Presenting a unified distance metric learning framework for adaptive hierarchical clustering. Wu et al. [9] ✕ ✓ ✓ ✕ TMM 2015 Exploring gradient directional symmetry at component level for smoothing edge components before text detection. Tian et al. [17] ✕ ✓ ✕ ✕ IJCAI 2016 Scene text is first detected locally in individual frames and finally linked by an optimal tracking trajectory. Yang et al. [33] ✕ ✓ ✓ ✕ TIP 2017 A text detector will locate character candidates and extract text regions. Then they will linked by an optimal tracking trajectory. Liang et al. [8] ✕ ✓ ✓ ✓ TIP 2015 Exploring maxima stable extreme regions along with stroke width transform for detecting candidate text regions. Michal et al.[12] FASText ✕ ✓ ✓ ✕ ICCV 2015 Stroke keypoints are efficiently detected and then exploited to obtain stroke segmentations. 2.1.2基于分割的方法 Method Model Code Hori Quad Irreg Source Time Highlight Li et al. [3] ✕ ✓ ✓ ✕ TIP 2014 (1)develop three novel cues that are tailored for character detection and a Bayesian method for their integration; (2)design a Markov random field model to exploit the inherent dependencies between characters. Zhang et al. [14] ✕ ✓ ✓ ✕ CVPR 2016 Utilizing FCN for salient map detection and centroid of each character prediction. Zhu et al. [16] ✕ ✓ ✓ ✕ CVPR 2016 Performs a graph-based segmentation of connected components into words (Word-Graph). He et al. [18] Text-CNN ✕ ✓ ✓ ✕ TIP 2016 Developing a new learning mechanism to train the Text-CNN with multi-level and rich supervised information. Yao et al. [21] ✕ ✓ ✓ ✕ arXiv 2016 Proposing to localize text in a holistic manner, by casting scene text detection as a semantic segmentation problem. Hu et al. [27] WordSup ✕ ✓ ✓ ✕ ICCV 2017 Proposing a weakly supervised framework that can utilize word annotations. Then the detected characters are fed to a text structure analysis module. Wu et al. [28] ✕ ✓ ✓ ✕ ICCV 2017 Introducing the border class to the text detection problem for the first time, and validate that the decoding process is largely simplified with the help of text border. Tang et al.[32] ✕ ✓ ✕ ✕ TIP 2017 A text-aware candidate text region(CTR) extraction model + CTR refinement model. Dai et al. [35] FTSN ✕ ✓ ✓ ✕ arXiv 2017 Detecting and segmenting the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Wang et al. [38] ✕ ✓ ✕ ✕ ICDAR 2017 This paper proposes a novel character candidate extraction method based on super-pixel segmentation and hierarchical clustering. Deng et al. [40] PixelLink ✓ ✓ ✓ ✕ AAAI 2018 Text instances are first segmented out by linking pixels wthin the same instance together. Liu et al. [42] MCN ✕ ✓ ✓ ✕ CVPR 2018 Stochastic Flow Graph (SFG) + Markov Clustering. Lyu et al. [43] ✕ ✓ ✓ ✕ CVPR 2018 Detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions. Chu et al. [45] Border ✕ ✓ ✓ ✕ ECCV 2018 The paper presents a novel scene text detection technique that makes use of semantics-aware text borders and bootstrapping based text segment augmentation. Long et al. [46] TextSnake ✕ ✓ ✓ ✓ ECCV 2018 The paper proposes TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms based on symmetry axis. Yang et al. [47] IncepText ✕ ✓ ✓ ✕ IJCAI 2018 Designing a novel Inception-Text module and introduce deformable PSROI pooling to deal with multi-oriented text detection. Yue et al. [48] ✕ ✓ ✓ ✕ BMVC 2018 Proposing a general framework for text detection called Guided CNN to achieve the two goals simultaneously. Zhong et al. [53] AF-RPN ✕ ✓ ✓ ✕ arXiv 2018 Presenting AF-RPN(anchor-free) as an anchor-free and scale-friendly region proposal network for the Faster R-CNN framework. Wang et al. [54] PSENet ✓ ✓ ✓ ✓ CVPR 2019 Proposing a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance. Xu et al.[57] TextField ✕ ✓ ✓ ✓ arXiv 2018 Presenting a novel direction field which can represent scene texts of arbitrary shapes. Tian et al. [58] FTDN ✕ ✓ ✓ ✕ ICIP 2018 FTDN is able to segment text region and simultaneously regress text box at pixel-level. Tian et al. [83] ✕ ✓ ✓ ✓ CVPR 2019 Constraining embedding feature of pixels inside the same text region to share similar properties. Huang et al. [4] MSERs-CNN ✕ ✓ ✕ ✕ ECCV 2014 Combining MSERs with CNN Sun et al. [6] ✕ ✓ ✕ ✕ PR 2015 Presenting a robust text detection approach based on color-enhanced CER and neural networks. Baek et al. [62] CRAFT ✕ ✓ ✓ ✓ CVPR 2019 Proposing CRAFT effectively detect text area by exploring each character and affinity between characters. Richardson et al. [87] ✕ ✓ ✓ ✕ WACV 2019 Presenting an additional scale predictor the estimate the better scale of text regions for testing. Wang et al. [88] SAST ✕ ✓ ✓ ✓ ACMM 2019 Presenting a context attended multi-task learning framework for scene text detection. Wang et al. [90] PAN ✕ ✓ ✓ ✓ ICCV 2019 Proposing an efficient and accurate arbitrary-shaped text detector called Pixel Aggregation Network(PAN), 2.1.3 基于回归的方法 Method Model Code Hori Quad Irreg Source Time Highlight Gupta et al. [15] FCRN ✓ ✓ ✕ ✕ CVPR 2016 (a) Proposing a fast and scalable engine to generate synthetic images of text in clutter; (b) FCRN. Zhong et al. [20] DeepText ✕ ✓ ✕ ✕ arXiv 2016 (a) Inception-RPN; (b) Utilize ambiguous text category (ATC) information and multilevel region-of-interest pooling (MLRP). Liao et al. [22] TextBoxes ✓ ✓ ✕ ✕ AAAI 2017 Mainly basing SSD object detection framework. Liu et al. [25] DMPNet ✕ ✓ ✓ ✕ CVPR 2017 Quadrilateral sliding windows + shared Monte-Carlo method for fast and accurate computing of the polygonal areas + a sequential protocol for relative regression. He et al. [26] DDR ✕ ✓ ✓ ✕ ICCV 2017 Proposing an FCN that has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. Jiang et al. [36] R2CNN ✕ ✓ ✓ ✕ arXiv 2017 Using the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Xing et al. [37] ArbiText ✕ ✓ ✓ ✕ arXiv 2017 Adopting the circle anchors and incorporating a pyramid pooling module into the Single Shot MultiBox Detector framework. Zhang et al. [39] FEN ✕ ✓ ✕ ✕ AAAI 2018 Proposing a refined scene text detector with a novel Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement. Wang et al. [41] ITN ✕ ✓ ✓ ✕ CVPR 2018 ITN is presented to learn the geometry-aware representation encoding the unique geometric configurations of scene text instances with in-network transformation embedding. Liao et al. [44] RRD ✕ ✓ ✓ ✕ CVPR 2018 The regression branch extracts rotation-sensitive features, while the classification branch extracts rotation-invariant features by pooling the rotation sensitive features. Liao et al. [49] TextBoxes++ ✓ ✓ ✓ ✕ TIP 2018 Mainly basing SSD object detection framework and it replaces the rectangular box representation in conventional object detector by a quadrilateral or oriented rectangle representation. He et al. [50] ✕ ✓ ✓ ✕ TIP 2018 Proposing a scene text detection framework based on fully convolutional network with a bi-task prediction module. Ma et al. [51] RRPN ✓ ✓ ✓ ✕ TMM 2018 RRPN + RRoI Pooling. Zhu et al. [55] SLPR ✕ ✓ ✓ ✓ arXiv 2018 SLPR regresses multiple points on the edge of text line and then utilizes these points to sketch the outlines of the text. Deng et al. [56] ✓ ✓ ✓ ✕ arXiv 2018 CRPN employs corners to estimate the possible locations of text instances. And it also designs a embedded data augmentation module inside region-wise subnetwork. Cai et al. [59] FFN ✕ ✓ ✕ ✕ ICIP 2018 Proposing a Feature Fusion Network to deal with text regions differing in enormous sizes. Sabyasachi et al. [60] RGC ✕ ✓ ✓ ✕ ICIP 2018 Proposing a novel recurrent architecture to improve the learnings of a feature map at a given time. Liu et al. [63] CTD ✓ ✓ ✓ ✓ PR 2019 CTD + TLOC + PNMS Xie et al. [79] DeRPN ✓ ✓ ✕ ✕ AAAI 2019 DeRPN utilizes anchor string mechanism instead of anchor box in RPN. Wang et al. [82] ✕ ✓ ✓ ✓ CVPR 2019 Text-RPN + RNN Liu et al. [84] ✕ ✓ ✓ ✓ CVPR 2019 CSE mechanism He et al. [29] SSTD ✓ ✓ ✓ ✕ ICCV 2017 Proposing an attention mechanism. Then developing a hierarchical inception module which efficiently aggregates multi-scale inception features. Tian et al. [11] ✕ ✓ ✕ ✕ ICCV 2015 Cascade boosting detects character candidates, and the min-cost flow network model get the final result. Tian et al. [13] CTPN ✓ ✓ ✕ ✕ ECCV 2016 1) RPN + LSTM. 2) RPN incorporate a new vertical anchor mechanism and LSTM connects the region to get the final result. He et al. [19] ✕ ✓ ✓ ✕ ACCV 2016 ER detetctor detects regions to get coarse prediction of text regions. Then the local context is aggregated to classify the remaining regions to obtain a final prediction. Shi et al. [23] SegLink ✓ ✓ ✓ ✕ CVPR 2017 Decomposing text into segments and links. A link connects two adjacent segments. Tian et al. [30] WeText ✕ ✓ ✕ ✕ ICCV 2017 Proposing a weakly supervised scene text detection method (WeText). Zhu et al. [31] RTN ✕ ✓ ✕ ✕ ICDAR 2017 Mainly basing CTPN vertical vertical proposal mechanism. Ren et al. [34] ✕ ✓ ✕ ✕ TMM 2017 Proposing a CNN-based detector. It contains a text structure component detector layer, a spatial pyramid layer, and a multi-input-layer deep belief network (DBN). Zhang et al. [10] ✕ ✓ ✕ ✕ CVPR 2015 The proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images. Wang et al. [86] DSRN ✕ ✓ ✓ ✕ IJCAI 2019 Presenting a scale-transfer module and scale relationship module to handle the problem of scale variation. Tang et al.[89] Seglink++ ✕ ✓ ✓ ✓ PR 2019 Presenting instance aware component grouping (ICG) for arbitrary-shape text detection. Wang et al.[92] ContourNet ✓ ✓ ✓ ✓ CVPR 2020 1.A scale-insensitive Adaptive Region Proposal Network (AdaptiveRPN); 2. Local Orthogonal Texture-aware Module (LOTM). 2.1.4 混合方法 Method Model Code Hori Quad Irreg Source Time Highlight Tang et al. [52] SSFT ✕ ✓ ✕ ✕ TMM 2018 Proposing a novel scene text detection method that involves superpixel-based stroke feature transform (SSFT) and deep learning based region classification (DLRC). Xie et al.[61] SPCNet ✕ ✓ ✓ ✓ AAAI 2019 Text Context module + Re-Score mechanism. Liu et al. [64] PMTD ✓ ✓ ✓ ✕ arXiv 2019 Perform “soft” semantic segmentation. It assigns a soft pyramid label (i.e., a real value between 0 and 1) for each pixel within text instance. Liu et al. [80] BDN ✓ ✓ ✓ ✕ IJCAI 2019 Discretizing bouding boxes into key edges to address label confusion for text detection. Zhang et al. [81] LOMO ✕ ✓ ✓ ✓ CVPR 2019 DR + IRM + SEM Zhou et al. [24] EAST ✓ ✓ ✓ ✕ CVPR 2017 The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images with instance segmentation. Yue et al. [48] ✕ ✓ ✓ ✕ BMVC 2018 Proposing a general framework for text detection called Guided CNN to achieve the two goals simultaneously. Zhong et al. [53] AF-RPN ✕ ✓ ✓ ✕ arXiv 2018 Presenting AF-RPN(anchor-free) as an anchor-free and scale-friendly region proposal network for the Faster R-CNN framework. Xue et al.[85] MSR ✕ ✓ ✓ ✓ IJCAI 2019 Presenting a noval multi-scale regression network. Liao et al. [91] DB ✓ ✓ ✓ ✓ AAAI 2020 Presenting differentiable binarization module to adaptively set the thresholds for binarization, which simplifies the post-processing. Xiao et al. [93] SDM ✕ ✓ ✓ ✓ ECCV 2020 1. A novel sequential deformation method; 2. auxiliary character counting supervision. 2.2 检测结果2.2.1 水平文本数据集的检测结果 Method Model Source Time Method Category IC11[68] IC13 [69] IC05[67] P R F P R F P R F Yao et al. [1] TD-Mixture CVPR 2012 Traditional ~ ~ ~ 0.69 0.66 0.67 ~ ~ ~ Yin et al. [2] TPAMI 2013 0.86 0.68 0.76 ~ ~ ~ ~ ~ ~ Yin et al. [7] TPAMI 2015 0.838 0.66 0.738 ~ ~ ~ ~ ~ ~ Wu et al. [9] TMM 2015 ~ ~ ~ 0.76 0.70 0.73 ~ ~ ~ Liang et al. [8] TIP 2015 0.77 0.68 0.71 0.76 0.68 0.72 ~ ~ ~ Michal et al.[12] FASText ICCV 2015 ~ ~ ~ 0.84 0.69 0.77 ~ ~ ~ Li et al. [3] TIP 2014 Segmentation 0.80 0.62 0.70 ~ ~ ~ ~ ~ ~ Zhang et al. [14] CVPR 2016 ~ ~ ~ 0.88 0.78 0.83 ~ ~ ~ He et al. [18] Text-CNN TIP 2016 0.91 0.74 0.82 0.93 0.73 0.82 0.87 0.73 0.79 Yao et al. [21] arXiv 2016 ~ ~ ~ 0.889 0.802 0.843 ~ ~ ~ Hu et al. [27] WordSup ICCV 2017 ~ ~ ~ 0.933 0.875 0.903 ~ ~ ~ Tang et al.[32] TIP 2017 0.90 0.86 0.88 0.92 0.87 0.89 ~ ~ ~ Wang et al. [38] ICDAR 2017 0.87 0.78 0.82 0.87 0.82 0.84 ~ ~ ~ Deng et al. [40] PixelLink AAAI 2018 ~ ~ ~ 0.886 0.875 0.881 ~ ~ ~ Liu et al. [42] MCN CVPR 2018 ~ ~ ~ 0.88 0.87 0.88 ~ ~ ~ Lyu et al. [43] CVPR 2018 ~ ~ ~ 0.92 0.844 0.880 ~ ~ ~ Chu et al. [45] Border ECCV 2018 ~ ~ ~ 0.915 0.871 0.892 ~ ~ ~ Wang et al. [54] PSENet CVPR 2019 ~ ~ ~ 0.94 0.90 0.92 ~ ~ ~ Huang et al. [4] MSERs-CNN ECCV 2014 0.88 0.71 0.78 ~ ~ ~ 0.84 0.67 0.75 Sun et al. [6] PR 2015 0.92 0.91 0.91 0.94 0.92 0.93 ~ ~ ~ Gupta et al. [15] FCRN CVPR 2016 Regression 0.94 0.77 0.85 0.938 0.764 0.842 ~ ~ ~ Zhong et al. [20] DeepText arXiv 2016 0.87 0.83 0.85 0.85 0.81 0.83 ~ ~ ~ Liao et al. [22] TextBoxes AAAI 2017 0.89 0.82 0.86 0.89 0.83 0.86 ~ ~ ~ Liu et al. [25] DMPNet CVPR 2017 ~ ~ ~ 0.93 0.83 0.870 ~ ~ ~ Jiang et al. [36] R2CNN arXiv 2017 ~ ~ ~ 0.92 0.81 0.86 ~ ~ ~ Xing et al. [37] ArbiText arXiv 2017 ~ ~ ~ 0.826 0.936 0.877 ~ ~ ~ Wang et al. [41] ITN CVPR 2018 0.896 0.889 0.892 0.941 0.893 0.916 ~ ~ ~ Liao et al. [49] TextBoxes++ TIP 2018 ~ ~ ~ 0.92 0.86 0.89 ~ ~ ~ He et al. [50] TIP 2018 ~ ~ ~ 0.91 0.84 0.88 ~ ~ ~ Ma et al. [51] RRPN TMM 2018 ~ ~ ~ 0.95 0.89 0.91 ~ ~ ~ Zhu et al. [55] SLPR arXiv 2018 ~ ~ ~ 0.90 0.72 0.80 ~ ~ ~ Cai et al. [59] FFN ICIP 2018 ~ ~ ~ 0.92 0.84 0.876 ~ ~ ~ Sabyasachi et al. [60] RGC ICIP 2018 ~ ~ ~ 0.89 0.77 0.83 ~ ~ ~ Wang et al. [82] CVPR 2019 ~ ~ ~ 0.937 0.878 0.907 ~ ~ ~ Liu et al. [84] CVPR 2019 ~ ~ ~ 0.937 0.897 0.917 ~ ~ ~ He et al. [29] SSTD ICCV 2017 ~ ~ ~ 0.89 0.86 0.88 ~ ~ ~ Tian et al. [11] ICCV 2015 0.86 0.76 0.81 0.852 0.759 0.802 ~ ~ ~ Tian et al. [13] CTPN ECCV 2016 ~ ~ ~ 0.93 0.83 0.88 ~ ~ ~ He et al. [19] ACCV 2016 ~ ~ ~ 0.90 0.75 0.81 ~ ~ ~ Shi et al. [23] SegLink CVPR 2017 ~ ~ ~ 0.877 0.83 0.853 ~ ~ ~ Tian et al. [30] WeText ICCV 2017 ~ ~ ~ 0.911 0.831 0.869 ~ ~ ~ Zhu et al. [31] RTN ICDAR 2017 ~ ~ ~ 0.94 0.89 0.91 ~ ~ ~ Ren et al. [34] TMM 2017 0.78 0.67 0.72 0.81 0.67 0.73 ~ ~ ~ Zhang et al. [10] CVPR 2015 0.84 0.76 0.80 0.88 0.74 0.80 ~ ~ ~ Tang et al. [52] SSFT TMM 2018 Hybrid 0.906 0.847 0.876 0.911 0.861 0.885 ~ ~ ~ Xie et al.[61] SPCNet AAAI 2019 ~ ~ ~ 0.94 0.91 0.92 ~ ~ ~ Liu et al. [80] BDN IJCAI 2019 ~ ~ ~ 0.887 0.894 0.89 ~ ~ ~ Zhou et al. [24] EAST CVPR 2017 ~ ~ ~ 0.93 0.83 0.870 ~ ~ ~ Yue et al. [48] BMVC 2018 ~ ~ ~ 0.885 0.846 0.870 ~ ~ ~ Zhong et al. [53] AF-RPN arXiv 2018 ~ ~ ~ 0.94 0.90 0.92 ~ ~ ~ Xue et al.[85] MSR IJCAI 2019 ~ ~ ~ 0.918 0.885 0.901 ~ ~ ~ 2.2.2 任意四边形文本数据集的检测结果 Method Model Source Time Method Category IC15 [70] MSRA-TD500 [71] USTB-SV1K [65] SVT [66] P R F P R F P R F P R F Le et al. [5] HOCC CVPR 2014 Traditional ~ ~ ~ 0.71 0.62 0.66 ~ ~ ~ ~ ~ ~ Yin et al. [7] TPAMI 2015 ~ ~ ~ 0.81 0.63 0.71 0.499 0.454 0.475 ~ ~ ~ Wu et al. [9] TMM 2015 ~ ~ ~ 0.63 0.70 0.66 ~ ~ ~ ~ ~ ~ Tian et al. [17] IJCAI 2016 ~ ~ ~ 0.95 0.58 0.721 0.537 0.488 0.51 ~ ~ ~ Yang et al. [33] TIP 2017 ~ ~ ~ 0.95 0.58 0.72 0.54 0.49 0.51 ~ ~ ~ Liang et al. [8] TIP 2015 ~ ~ ~ 0.74 0.66 0.70 ~ ~ ~ ~ ~ ~ Zhang et al. [14] CVPR 2016 Segmentation 0.71 0.43 0.54 0.83 0.67 0.74 ~ ~ ~ ~ ~ ~ Zhu et al. [16] CVPR 2016 0.81 0.91 0.85 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [18] Text-CNN TIP 2016 ~ ~ ~ 0.76 0.61 0.69 ~ ~ ~ ~ ~ ~ Yao et al. [21] arXiv 2016 0.723 0.587 0.648 0.765 0.753 0.759 ~ ~ ~ ~ ~ ~ Hu et al. [27] WordSup ICCV 2017 0.793 0.77 0.782 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wu et al. [28] ICCV 2017 0.91 0.78 0.84 0.77 0.78 0.77 ~ ~ ~ ~ ~ ~ Dai et al. [35] FTSN arXiv 2017 0.886 0.80 0.841 0.876 0.771 0.82 ~ ~ ~ ~ ~ ~ Deng et al. [40] PixelLink AAAI 2018 0.855 0.820 0.837 0.830 0.732 0.778 ~ ~ ~ ~ ~ ~ Liu et al. [42] MCN CVPR 2018 0.72 0.80 0.76 0.88 0.79 0.83 ~ ~ ~ ~ ~ ~ Lyu et al. [43] CVPR 2018 0.895 0.797 0.843 0.876 0.762 0.815 ~ ~ ~ ~ ~ ~ Chu et al. [45] Border ECCV 2018 ~ ~ ~ 0.830 0.774 0.801 ~ ~ ~ ~ ~ ~ Long et al. [46] TextSnake ECCV 2018 0.849 0.804 0.826 0.832 0.739 0.783 ~ ~ ~ ~ ~ ~ Yang et al. [47] IncepText IJCAI 2018 0.938 0.873 0.905 0.875 0.790 0.830 ~ ~ ~ ~ ~ ~ Wang et al. [54] PSENet CVPR 2019 0.8692 0.845 0.8569 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xu et al.[57] TextField arXiv 2018 0.843 0.805 0.824 0.874 0.759 0.813 ~ ~ ~ ~ ~ ~ Tian et al. [58] FTDN ICIP 2018 0.847 0.773 0.809 ~ ~ ~ ~ ~ ~ ~ ~ ~ Tian et al. [83] CVPR 2019 0.883 0.850 0.866 0.842 0.817 0.829 ~ ~ ~ ~ ~ ~ Baek et al. [62] CRAFT CVPR 2019 0.898 0.843 0.869 0.882 0.782 0.829 ~ ~ ~ ~ ~ ~ Richardson et al. [87] IJCAI 2019 0.853 0.83 0.827 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wang et al. [88] SAST ACMM 2019 0.8755 0.8734 0.8744 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wang et al. [90] PAN ICCV 2019 0.84 0.819 0.829 0.844 0.838 0.821 ~ ~ ~ ~ ~ ~ Gupta et al. [15] FCRN CVPR 2016 Regression ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.651 0.599 0.624 Liu et al. [25] DMPNet CVPR 2017 0.732 0.682 0.706 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [26] DDR ICCV 2017 0.82 0.80 0.81 0.77 0.70 0.74 ~ ~ ~ ~ ~ ~ Jiang et al. [36] R2CNN arXiv 2017 0.856 0.797 0.825 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xing et al. [37] ArbiText arXiv 2017 0.792 0.735 0.759 0.78 0.72 0.75 ~ ~ ~ ~ ~ ~ Wang et al. [41] ITN CVPR 2018 0.857 0.741 0.795 0.903 0.723 0.803 ~ ~ ~ ~ ~ ~ Liao et al. [44] RRD CVPR 2018 0.88 0.8 0.838 0.876 0.73 0.79 ~ ~ ~ ~ ~ ~ Liao et al. [49] TextBoxes++ TIP 2018 0.878 0.785 0.829 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [50] TIP 2018 0.85 0.80 0.82 0.91 0.81 0.86 ~ ~ ~ ~ ~ ~ Ma et al. [51] RRPN TMM 2018 0.822 0.732 0.774 0.821 0.677 0.742 ~ ~ ~ ~ ~ ~ Zhu et al. [55] SLPR arXiv 2018 0.855 0.836 0.845 ~ ~ ~ ~ ~ ~ ~ ~ ~ Deng et al. [56] arXiv 2018 0.89 0.81 0.845 ~ ~ ~ ~ ~ ~ ~ ~ ~ Sabyasachi et al. [60] RGC ICIP 2018 0.83 0.81 0.82 0.85 0.76 0.80 ~ ~ ~ ~ ~ ~ Wang et al. [82] CVPR 2019 0.892 0.86 0.876 0.852 0.821 0.836 ~ ~ ~ ~ ~ ~ He et al. [29] SSTD ICCV 2017 0.80 0.73 0.77 ~ ~ ~ ~ ~ ~ ~ ~ ~ Tian et al. [13] CTPN ECCV 2016 0.74 0.52 0.61 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [19] ACCV 2016 ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.87 0.73 0.79 Shi et al. [23] SegLink CVPR 2017 0.731 0.768 0.75 0.86 0.70 0.77 ~ ~ ~ ~ ~ ~ Wang et al. [86] DSRN IJCAI 2019 0.832 0.796 0.814 0.876 0.712 0.785 ~ ~ ~ ~ ~ ~ Tang et al.[89] Seglink++ PR 2019 0.837 0.803 0.820 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wang et al. [92] ContourNet CVPR 2020 0.876 0.861 0.869 ~ ~ ~ ~ ~ ~ ~ ~ ~ Tang et al. [52] SSFT TMM 2018 Hybrid ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.541 0.758 0.631 Xie et al.[61] SPCNet AAAI 2019 0.89 0.86 0.87 ~ ~ ~ ~ ~ ~ ~ ~ ~ Liu et al. [64] PMTD arXiv 2019 0.913 0.874 0.893 ~ ~ ~ ~ ~ ~ ~ ~ ~ Liu et al. [80] BDN IJCAI 2019 0.881 0.846 0.863 0.87 0.815 0.842 ~ ~ ~ ~ ~ ~ Zhang et al. [81] LOMO CVPR 2019 0.878 0.876 0.877 ~ ~ ~ ~ ~ ~ ~ ~ ~ Zhou et al. [24] EAST CVPR 2017 0.833 0.783 0.807 0.873 0.674 0.761 ~ ~ ~ ~ ~ ~ Yue et al. [48] BMVC 2018 0.866 0.789 0.823 ~ ~ ~ ~ ~ ~ 0.691 0.660 0.675 Zhong et al. [53] AF-RPN arXiv 2018 0.89 0.83 0.86 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xue et al.[85] MSR IJCAI 2019 ~ ~ ~ 0.874 0.767 0.817 ~ ~ ~ ~ ~ ~ Liao et al. [91] DB AAAI 2020 0.918 0.832 0.873 0.915 0.792 0.849 ~ ~ ~ ~ ~ ~ Xiao et al. [93] SDM ECCV 2020 0.9196 0.8922 0.9057 ~ ~ ~ ~ ~ ~ ~ ~ ~ Method Model Source Time Method Category IC15 [70] MSRA-TD500 [71] USTB-SV1K [65] SVT [66] P R F P R F P R F P R F Le et al. [5] HOCC CVPR 2014 Traditional ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.80 0.73 0.76 Yao et al. [21] arXiv 2016 Segmentation 0.432 0.27 0.333 ~ ~ ~ ~ ~ ~ ~ ~ ~ Hu et al. [27] WordSup ICCV 2017 0.452 0.309 0.368 ~ ~ ~ ~ ~ ~ ~ ~ ~ Lyu et al. [43] CVPR 2018 0.351 0.348 0.349 ~ ~ ~ 0.743 0.706 0.724 ~ ~ ~ Chu et al. [45] Border ECCV 2018 ~ ~ ~ 0.782 0.588 0.671 0.777 0.621 0.690 ~ ~ ~ Yang et al. [47] IncepText IJCAI 2018 ~ ~ ~ 0.785 0.569 0.660 ~ ~ ~ ~ ~ ~ Wang et al. [54] PSENet CVPR 2019 ~ ~ ~ ~ ~ ~ 0.7535 0.6918 0.7213 ~ ~ ~ Baek et al. [62] CRAFT CVPR 2019 ~ ~ ~ ~ ~ ~ 0.806 0.682 0.739 ~ ~ ~ He et al. [29] SSTD ICCV 2017 Regression 0.46 0.31 0.37 ~ ~ ~ ~ ~ ~ ~ ~ ~ Gupta et al. [15] FCRN CVPR 2016 ~ ~ ~ ~ ~ ~ 0.844 0.763 0.801 ~ ~ ~ Liao et al. [49] TextBoxes++ TIP 2018 0.61 0.57 0.59 ~ ~ ~ ~ ~ ~ ~ ~ ~ Ma et al. [51] RRPN TMM 2018 ~ ~ ~ ~ ~ ~ 0.7669 0.5794 0.6601 ~ ~ ~ Deng et al. [56] arXiv 2018 0.555 0.633 0.591 ~ ~ ~ ~ ~ ~ ~ ~ ~ Cai et al. [59] FFN ICIP 2018 0.43 0.35 0.39 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xie et al. [79] DeRPN AAAI 2019 0.586 0.557 0.571 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [29] SSTD ICCV 2017 0.46 0.31 0.37 ~ ~ ~ ~ ~ ~ ~ ~ ~ Liao et al. [44] RRD CVPR 2018 ~ ~ ~ 0.591 0.775 0.670 ~ ~ ~ ~ ~ ~ Richardson et al. [87] IJCAI 2019 ~ ~ ~ ~ ~ ~ 0.729 0.618 0.669 ~ ~ ~ Wang et al. [88] SAST ACMM 2019 ~ ~ ~ ~ ~ ~ 0.7935 0.6653 0.7237 ~ ~ ~ Xie et al.[61] SPCNet AAAI 2019 Hybrid ~ ~ ~ ~ ~ ~ 0.806 0.686 0.741 ~ ~ ~ Liu et al. [64] PMTD arXiv 2019 ~ ~ ~ ~ ~ ~ 0.844 0.763 0.801 ~ ~ ~ Liu et al. [80] BDN IJCAI 2019 ~ ~ ~ ~ ~ ~ 0.791 0.698 0.742 ~ ~ ~ Zhang et al. [81] LOMO CVPR 2019 ~ ~ ~ 0.791 0.602 0.684 0.802 0.672 0.731 ~ ~ ~ Zhou et al. [24] EAST CVPR 2017 0.504 0.324 0.395 ~ ~ ~ ~ ~ ~ ~ ~ ~ Zhong et al. [53] AF-RPN arXiv 2018 ~ ~ ~ ~ ~ ~ 0.75 0.66 0.70 ~ ~ ~ Liao et al. [91] DB AAAI 2020 ~ ~ ~ ~ ~ ~ 0.831 0.679 0.747 ~ ~ ~ Xiao et al. [93] SDM ECCV 2020 ~ ~ ~ ~ ~ ~ 0.8679 0.7526 0.8061 ~ ~ ~ 2.2.3 不规则文本数据集的检测结果在本节中，我们仅选择适用于不规则文本检测的那些方法。 Method Model Source Time Method Category Total-text [74] SCUT-CTW1500 [75] P R F P R F Baek et al. [62] CRAFT CVPR 2019 Segmentation 0.876 0.799 0.836 0.860 0.811 0.835 Long et al. [46] TextSnake ECCV 2018 0.827 0.745 0.784 0.679 0.853 0.756 Tian et al. [83] CVPR 2019 ~ ~ ~ 81.7 84.2 80.1 Wang et al. [54] PSENet CVPR 2019 0.840 0.779 0.809 0.848 0.797 0.822 Wang et al. [88] SAST ACMM 2019 0.8557 0.7549 0.802 0.8119 0.8171 0.8145 Wang et al. [90] PAN ICCV 2019 0.893 0.81 0.85 0.864 0.812 0.837 Zhu et al. [55] SLPR arXiv 2018 Regression ~ ~ ~ 0.801 0.701 0.748 Liu et al. [63] CTD+TLOC PR 2019 ~ ~ ~ 0.774 0.698 0.734 Wang et al. [82] CVPR 2019 ~ ~ ~ 80.1 80.2 80.1 Liu et al. [84] CVPR 2019 0.814 0.791 0.802 0.787 0.761 0.774 Tang et al.[89] Seglink++ PR 2019 0.829 0.809 0.815 0.828 0.798 0.813 Wang et al. [92] ContourNet CVPR 2020 0.869 0.839 0.854 0.837 0.841 0.839 Zhang et al. [81] LOMO CVPR 2019 Hybrid 0.876 0.793 0.833 0.857 0.765 0.808 Xie et al.[61] SPCNet AAAI 2019 0.83 0.83 0.83 ~ ~ ~ Xue et al.[85] MSR IJCAI 2019 0.852 0.73 0.768 0.838 0.778 0.807 Liao et al. [91] DB AAAI 2020 0.871 0.825 0.847 0.869 0.802 0.834 Xiao et al.[93] SDM ECCV 2020 0.9085 0.8603 0.8837 0.884 0.8442 0.8636 3. 综述[A] [TPAMI-2015] Ye Q, Doermann D. Text detection and recognition in imagery: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(7): 1480-1500. paper[B] [Frontiers-Comput. Sci-2016] Zhu Y, Yao C, Bai X. Scene text detection and recognition: Recent advances and future trends[J]. Frontiers of Computer Science, 2016, 10(1): 19-36. paper[C] [arXiv-2018] Long S, He X, Ya C. Scene Text Detection and Recognition: The Deep Learning Era[J]. arXiv preprint arXiv:1811.04256, 2018. paper4. Evaluation如果您有兴趣开发更好的场景文本检测指标，那么这里推荐的一些参考可能会有用：[A] Wolf, Christian, and Jean-Michel Jolion. "Object count/area graphs for the evaluation of object detection and segmentation algorithms." International Journal of Document Analysis and Recognition (IJDAR) 8.4 (2006): 280-296. paper[B] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. K. Ghosh, A. D.Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu, F. Shafait, S. Uchida, and E. Valveny. ICDAR 2015 competition on robust reading. In ICDAR, pages 1156–1160, 2015. paper[C] Calarasanu, Stefania, Jonathan Fabrizio, and Severine Dubuisson. "What is a good evaluation protocol for text localization systems? Concerns, arguments, comparisons and solutions." Image and Vision Computing 46 (2016): 1-17. paper[D] Shi, Baoguang, et al. "ICDAR2017 competition on reading chinese text in the wild (RCTW-17)." 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Vol. 1. IEEE, 2017. paper[E] Nayef, N; Yin, F; Bizid, I; et al. ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-rrc-mlt. In Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, volume 1, 1454–1459. IEEE.paper[F] Dangla, Aliona, et al. "A first step toward a fair comparison of evaluation protocols for text detection algorithms." 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2018. paper[G] He,Mengchao and Liu, Yuliang, et al. ICPR2018 Contest on Robust Reading for Multi-Type Web images. ICPR 2018. paper[H] Liu, Yuliang and Jin, Lianwen, et al. "Tightness-aware Evaluation Protocol for Scene Text Detection" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019. paper code5. 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Coco-text: Dataset and benchmark for text detection and recognition in natural images. arXiv preprint arXiv:1601.07140, 2016. PaperRCTW-17[73]：Shi B, Yao C, Liao M, et al. ICDAR2017 competition on reading chinese text in the wild (RCTW-17). Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, 2017, 1: 1429-1434. PaperTotal-Text[74]：Chee C K, Chan C S. Total-text: A comprehensive dataset for scene text detection and recognition.Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, 2017, 1: 935-942.PaperSCUT-CTW1500[75]：Yuliang L, Lianwen J, Shuaitao Z, et al. Curved Scene Text Detection via Transverse and Longitudinal Sequence Connection. Pattern Recognition, 2019.PaperMLT 2017[76]: Nayef, N; Yin, F; Bizid, I; et al. ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-rrc-mlt. 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Scene Text Detection Resources(场景文字识别资源汇总)[转载] 1. Datasets1.1 Horizontal-Text DatasetsICDAR 2003(IC03)：Introduction: It contains 509 images in total, 258 for training and 251 for testing. Specifically, it contains 1110 text instance in training set, while 1156 in testing set. It has word-level annotation. IC03 only consider English text instance.Link: IC03-downloadICDAR 2011(IC11):Introduction: IC11 is an English dataset for text detection. It contains 484 images, 229 for training and 255 for testing. There are 1564 text instance in this dataset. It provides both word-level and character-level annotation.Link: IC11-downloadICDAR 2013(IC13)：Introduction: IC13 is almost the same as IC11. It contains 462 images in total, 229 for training and 233 for testing. Specifically, it contains 849 text instance in training set, while 1095 in testing set.Link: IC13-download1.2 Arbitrary-Quadrilateral-Text DatasetsUSTB-SV1K：Introduction: USTB-SV1K is an English dataset. It contains 1000 street images from Google Street View with 2955 text instance in total. It only provides word-level annotations.Link: USTB-SV1K-downloadSVT：Introduction: It contains 350 images with 725 English text intance in total. SVT has both character-level and word-level annotations. The images of SVT are harvested from Google Street View and have low resolution.Link: SVT-downloadSVT-P：Introduction: It contains 639 cropped word images for testing. Images were selected from the side-view angle snapshots in Google Street View. Therefore, most images are heavily distorted by the non-frontal view angle. It is the imporved datasets of SVT.Link: SVT-P-download (Password : vnis)ICDAR 2015(IC15)：Introduction: It contains 1500 images in total, 1000 for training and 500 for testing. Specifically, it contains 17548 text instance. It provides word-level annotations. IC15 is the first incidental scene text dataset and it only considers English words.Link: IC15-downloadCOCO-Text：Introduction: It contains 63686 images in total, 43686 for training, 10000 for validating and 10000 for testing. Specifically, it contains 145859 cropped word images for testing, including handwritten and printed, clear and blur, English and non-English.Link: COCO-Text-downloadMSRA-TD500：Introduction: It contains 500 images in total. It provides text-line-level annotation rather than word, and polygon boxes rather than axis-aligned rectangles for text region annootation. It contains both English and Chinese text instance.Link: MSRA-TD500-downloadMLT 2017：Introduction: It contains 10000 natural images in total. It provides word-level annotation. There are 9 languages for MLT. It is a more real and complex datasets for scene text detection and recognition..Link: MLT-downloadMLT 2019:Introduction: It contains 18000 images in total. It provides word-level annotation. Compared to MLT, this dataset has 10 languages. It is a more real and complex datasets for scene text detection and recognition..Link: MLT-2019-downloadCTW：Introduction: It contains 32285 high resolution street view images of Chinese text, with 1018402 character instances in total. All images are annotated at the character level, including its underlying character type, bouding box, and 6 other attributes. These attributes indicate whether its background is complex, whether it’s raised, whether it’s hand-written or printed, whether it’s occluded, whether it’s distorted, whether it uses word-art.Link: CTW-downloadRCTW-17：Introduction: It contains 12514 images in total, 11514 for training and 1000 for testing. Images in RCTW-17 were mostly collected by camera or mobile phone, and others were generated images. Text instances are annotated with parallelograms. It is the first large scale Chinese dataset, and was also the largest published one by then.Link: RCTW-17-downloadReCTS：Introduction: This data set is a large-scale Chinese Street View Trademark Data Set. It is based on Chinese words and Chinese text line-level labeling. The labeling method is arbitrary quadrilateral labeling. It contains 20000 images in total.Link: ReCTS-download1.3 Irregular-Text DatasetsCUTE80：Introduction: It contains 80 high-resolution images taken in natural scenes. Specifically, it contains 288 cropped word images for testing. The dataset focuses on curved text. No lexicon is provided.Link: CUTE80-downloadTotal-Text：Introduction: It contains 1,555 images in total. Specifically, it contains 11,459 cropped word images with more than three different text orientations: horizontal, multi-oriented and curved.Link: Total-Text-downloadSCUT-CTW1500：Introduction: It contains 1500 images in total, 1000 for training and 500 for testing. Specifically, it contains 10751 cropped word images for testing. Annotations in CTW-1500 are polygons with 14 vertexes. The dataset mainly consists of Chinese and English.Link: CTW-1500-downloadLSVT：Introduction: LSVT consists of 20,000 testing data, 30,000 training data in full annotations and 400,000 training data in weak annotations, which are referred to as partial labels. The labeled text regions demonstrate the diversity of text: horizontal, multi-oriented and curved.Link: LSVT-downloadArTs：Introduction: ArT consists of 10,166 images, 5,603 for training and 4,563 for testing. They were collected with text shape diversity in mind and all text shapes have high number of existence in ArT.Link: ArT-download1.4 Synthetic DatasetsSynth80k :Introduction: It contains 800 thousands images with approximately 8 million synthetic word instances. Each text instance is annotated with its text-string, word-level and character-level bounding-boxes.Link: Synth80k-downloadSynthText :Introduction: It contains 6 million cropped word images. The generation process is similar to that of Synth90k. It is also annotated in horizontal-style.Link: SynthText-download1.5 Comparison of Datasets Comparison of Datasets Datasets Language Image Text instance Text Shape Annotation level Total Train Test Total Train Test Horizontal Arbitrary-Quadrilateral Multi-oriented Char Word Text-Line IC03 English 509 258 251 2266 1110 1156 ✓ ✕ ✕ ✕ ✓ ✕ IC11 English 484 229 255 1564 ～ ～ ✓ ✕ ✕ ✓ ✓ ✕ IC13 English 462 229 233 1944 849 1095 ✓ ✕ ✕ ✓ ✓ ✕ USTB-SV1K English 1000 500 500 2955 ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ SVT English 350 100 250 725 211 514 ✓ ✓ ✕ ✓ ✓ ✕ SVT-P English 238 ～ ～ 639 ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ IC15 English 1500 1000 500 17548 122318 5230 ✓ ✓ ✕ ✕ ✓ ✕ COCO-Text English 63686 43686 20000 145859 118309 27550 ✓ ✓ ✕ ✕ ✓ ✕ MSRA-TD500 English/Chinese 500 300 200 ～ ～ ～ ✓ ✓ ✕ ✕ ✕ ✓ MLT 2017 Multi-lingual 18000 7200 10800 ～ ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ MLT 2019 Multi-lingual 20000 10000 10000 ～ ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ CTW Chinese 32285 25887 6398 1018402 812872 205530 ✓ ✓ ✕ ✓ ✓ ✕ RCTW-17 English/Chinese 12514 15114 1000 ～ ～ ～ ✓ ✓ ✕ ✕ ✕ ✓ ReCTS Chinese 20000 ～ ～ ～ ～ ～ ✓ ✓ ✕ ✓ ✓ ✕ CUTE80 English 80 ～ ～ ～ ～ ～ ✕ ✕ ✓ ✕ ✓ ✓ Total-Text English 1525 1225 300 9330 ～ ～ ✓ ✓ ✓ ✕ ✓ ✓ CTW-1500 English/Chinese 1500 1000 500 10751 ～ ～ ✓ ✓ ✓ ✕ ✓ ✓ LSVT English/Chinese 450000 430000 20000 ～ ～ ～ ✓ ✓ ✓ ✕ ✓ ✓ ArT English/Chinese 10166 5603 4563 ～ ～ ～ ✓ ✓ ✓ ✕ ✓ ✕ Synth80k English 80k ～ ～ 8m ～ ～ ✓ ✕ ✕ ✓ ✓ ✕ SynthText English 800k ～ ～ 6m ～ ～ ✓ ✓ ✕ ✕ ✓ ✕ 2. Summary of Scene Text Detection Resources2.1 Comparison of MethodsScene text detection methods can be devided into four parts:(a) Traditional methods;(b) Segmentation-based methods;(c) Regression-based methods;(d) Hybrid methods.It is important to notice that: (1) "Hori" stands for horizontal scene text datasets. (2) "Quad" stands for arbitrary-quadrilateral-text datasets. (3) "Irreg" stands for irregular scence text datasets. (4) "Traditional method" stands for the methods that don't rely on deep learning.2.1.1 Traditional Methods Method Model Code Hori Quad Irreg Source Time Highlight Yao et al. [1] TD-Mixture ✕ ✓ ✓ ✕ CVPR 2012 1) A new dataset MSRA-TD500 and protocol for evaluation. 2) Equipped a two-level classification scheme and two sets of features extractor. Yin et al. [2] ✕ ✓ ✕ ✕ TPAMI 2013 Extract Maximally Stable Extremal Regions (MSERs) as character candidates and group them together. Le et al. [5] HOCC ✕ ✓ ✓ ✕ CVPR 2014 HOCC + MSERs Yin et al. [7] ✕ ✓ ✓ ✕ TPAMI 2015 Presenting a unified distance metric learning framework for adaptive hierarchical clustering. Wu et al. [9] ✕ ✓ ✓ ✕ TMM 2015 Exploring gradient directional symmetry at component level for smoothing edge components before text detection. Tian et al. [17] ✕ ✓ ✕ ✕ IJCAI 2016 Scene text is first detected locally in individual frames and finally linked by an optimal tracking trajectory. Yang et al. [33] ✕ ✓ ✓ ✕ TIP 2017 A text detector will locate character candidates and extract text regions. Then they will linked by an optimal tracking trajectory. Liang et al. [8] ✕ ✓ ✓ ✓ TIP 2015 Exploring maxima stable extreme regions along with stroke width transform for detecting candidate text regions. Michal et al.[12] FASText ✕ ✓ ✓ ✕ ICCV 2015 Stroke keypoints are efficiently detected and then exploited to obtain stroke segmentations. 2.1.2 Segmentation-based Methods Method Model Code Hori Quad Irreg Source Time Highlight Li et al. [3] ✕ ✓ ✓ ✕ TIP 2014 (1)develop three novel cues that are tailored for character detection and a Bayesian method for their integration; (2)design a Markov random field model to exploit the inherent dependencies between characters. Zhang et al. [14] ✕ ✓ ✓ ✕ CVPR 2016 Utilizing FCN for salient map detection and centroid of each character prediction. Zhu et al. [16] ✕ ✓ ✓ ✕ CVPR 2016 Performs a graph-based segmentation of connected components into words (Word-Graph). He et al. [18] Text-CNN ✕ ✓ ✓ ✕ TIP 2016 Developing a new learning mechanism to train the Text-CNN with multi-level and rich supervised information. Yao et al. [21] ✕ ✓ ✓ ✕ arXiv 2016 Proposing to localize text in a holistic manner, by casting scene text detection as a semantic segmentation problem. Hu et al. [27] WordSup ✕ ✓ ✓ ✕ ICCV 2017 Proposing a weakly supervised framework that can utilize word annotations. Then the detected characters are fed to a text structure analysis module. Wu et al. [28] ✕ ✓ ✓ ✕ ICCV 2017 Introducing the border class to the text detection problem for the first time, and validate that the decoding process is largely simplified with the help of text border. Tang et al.[32] ✕ ✓ ✕ ✕ TIP 2017 A text-aware candidate text region(CTR) extraction model + CTR refinement model. Dai et al. [35] FTSN ✕ ✓ ✓ ✕ arXiv 2017 Detecting and segmenting the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Wang et al. [38] ✕ ✓ ✕ ✕ ICDAR 2017 This paper proposes a novel character candidate extraction method based on super-pixel segmentation and hierarchical clustering. Deng et al. [40] PixelLink ✓ ✓ ✓ ✕ AAAI 2018 Text instances are first segmented out by linking pixels wthin the same instance together. Liu et al. [42] MCN ✕ ✓ ✓ ✕ CVPR 2018 Stochastic Flow Graph (SFG) + Markov Clustering. Lyu et al. [43] ✕ ✓ ✓ ✕ CVPR 2018 Detect scene text by localizing corner points of text bounding boxes and segmenting text regions in relative positions. Chu et al. [45] Border ✕ ✓ ✓ ✕ ECCV 2018 The paper presents a novel scene text detection technique that makes use of semantics-aware text borders and bootstrapping based text segment augmentation. Long et al. [46] TextSnake ✕ ✓ ✓ ✓ ECCV 2018 The paper proposes TextSnake, which is able to effectively represent text instances in horizontal, oriented and curved forms based on symmetry axis. Yang et al. [47] IncepText ✕ ✓ ✓ ✕ IJCAI 2018 Designing a novel Inception-Text module and introduce deformable PSROI pooling to deal with multi-oriented text detection. Yue et al. [48] ✕ ✓ ✓ ✕ BMVC 2018 Proposing a general framework for text detection called Guided CNN to achieve the two goals simultaneously. Zhong et al. [53] AF-RPN ✕ ✓ ✓ ✕ arXiv 2018 Presenting AF-RPN(anchor-free) as an anchor-free and scale-friendly region proposal network for the Faster R-CNN framework. Wang et al. [54] PSENet ✓ ✓ ✓ ✓ CVPR 2019 Proposing a novel Progressive Scale Expansion Network (PSENet), designed as a segmentation-based detector with multiple predictions for each text instance. Xu et al.[57] TextField ✕ ✓ ✓ ✓ arXiv 2018 Presenting a novel direction field which can represent scene texts of arbitrary shapes. Tian et al. [58] FTDN ✕ ✓ ✓ ✕ ICIP 2018 FTDN is able to segment text region and simultaneously regress text box at pixel-level. Tian et al. [83] ✕ ✓ ✓ ✓ CVPR 2019 Constraining embedding feature of pixels inside the same text region to share similar properties. Huang et al. [4] MSERs-CNN ✕ ✓ ✕ ✕ ECCV 2014 Combining MSERs with CNN Sun et al. [6] ✕ ✓ ✕ ✕ PR 2015 Presenting a robust text detection approach based on color-enhanced CER and neural networks. Baek et al. [62] CRAFT ✕ ✓ ✓ ✓ CVPR 2019 Proposing CRAFT effectively detect text area by exploring each character and affinity between characters. Richardson et al. [87] ✕ ✓ ✓ ✕ WACV 2019 Presenting an additional scale predictor the estimate the better scale of text regions for testing. Wang et al. [88] SAST ✕ ✓ ✓ ✓ ACMM 2019 Presenting a context attended multi-task learning framework for scene text detection. Wang et al. [90] PAN ✕ ✓ ✓ ✓ ICCV 2019 Proposing an efficient and accurate arbitrary-shaped text detector called Pixel Aggregation Network(PAN), 2.1.3 Regression-based Methods Method Model Code Hori Quad Irreg Source Time Highlight Gupta et al. [15] FCRN ✓ ✓ ✕ ✕ CVPR 2016 (a) Proposing a fast and scalable engine to generate synthetic images of text in clutter; (b) FCRN. Zhong et al. [20] DeepText ✕ ✓ ✕ ✕ arXiv 2016 (a) Inception-RPN; (b) Utilize ambiguous text category (ATC) information and multilevel region-of-interest pooling (MLRP). Liao et al. [22] TextBoxes ✓ ✓ ✕ ✕ AAAI 2017 Mainly basing SSD object detection framework. Liu et al. [25] DMPNet ✕ ✓ ✓ ✕ CVPR 2017 Quadrilateral sliding windows + shared Monte-Carlo method for fast and accurate computing of the polygonal areas + a sequential protocol for relative regression. He et al. [26] DDR ✕ ✓ ✓ ✕ ICCV 2017 Proposing an FCN that has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. Jiang et al. [36] R2CNN ✕ ✓ ✓ ✕ arXiv 2017 Using the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Xing et al. [37] ArbiText ✕ ✓ ✓ ✕ arXiv 2017 Adopting the circle anchors and incorporating a pyramid pooling module into the Single Shot MultiBox Detector framework. Zhang et al. [39] FEN ✕ ✓ ✕ ✕ AAAI 2018 Proposing a refined scene text detector with a novel Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement. Wang et al. [41] ITN ✕ ✓ ✓ ✕ CVPR 2018 ITN is presented to learn the geometry-aware representation encoding the unique geometric configurations of scene text instances with in-network transformation embedding. Liao et al. [44] RRD ✕ ✓ ✓ ✕ CVPR 2018 The regression branch extracts rotation-sensitive features, while the classification branch extracts rotation-invariant features by pooling the rotation sensitive features. Liao et al. [49] TextBoxes++ ✓ ✓ ✓ ✕ TIP 2018 Mainly basing SSD object detection framework and it replaces the rectangular box representation in conventional object detector by a quadrilateral or oriented rectangle representation. He et al. [50] ✕ ✓ ✓ ✕ TIP 2018 Proposing a scene text detection framework based on fully convolutional network with a bi-task prediction module. Ma et al. [51] RRPN ✓ ✓ ✓ ✕ TMM 2018 RRPN + RRoI Pooling. Zhu et al. [55] SLPR ✕ ✓ ✓ ✓ arXiv 2018 SLPR regresses multiple points on the edge of text line and then utilizes these points to sketch the outlines of the text. Deng et al. [56] ✓ ✓ ✓ ✕ arXiv 2018 CRPN employs corners to estimate the possible locations of text instances. And it also designs a embedded data augmentation module inside region-wise subnetwork. Cai et al. [59] FFN ✕ ✓ ✕ ✕ ICIP 2018 Proposing a Feature Fusion Network to deal with text regions differing in enormous sizes. Sabyasachi et al. [60] RGC ✕ ✓ ✓ ✕ ICIP 2018 Proposing a novel recurrent architecture to improve the learnings of a feature map at a given time. Liu et al. [63] CTD ✓ ✓ ✓ ✓ PR 2019 CTD + TLOC + PNMS Xie et al. [79] DeRPN ✓ ✓ ✕ ✕ AAAI 2019 DeRPN utilizes anchor string mechanism instead of anchor box in RPN. Wang et al. [82] ✕ ✓ ✓ ✓ CVPR 2019 Text-RPN + RNN Liu et al. [84] ✕ ✓ ✓ ✓ CVPR 2019 CSE mechanism He et al. [29] SSTD ✓ ✓ ✓ ✕ ICCV 2017 Proposing an attention mechanism. Then developing a hierarchical inception module which efficiently aggregates multi-scale inception features. Tian et al. [11] ✕ ✓ ✕ ✕ ICCV 2015 Cascade boosting detects character candidates, and the min-cost flow network model get the final result. Tian et al. [13] CTPN ✓ ✓ ✕ ✕ ECCV 2016 1) RPN + LSTM. 2) RPN incorporate a new vertical anchor mechanism and LSTM connects the region to get the final result. He et al. [19] ✕ ✓ ✓ ✕ ACCV 2016 ER detetctor detects regions to get coarse prediction of text regions. Then the local context is aggregated to classify the remaining regions to obtain a final prediction. Shi et al. [23] SegLink ✓ ✓ ✓ ✕ CVPR 2017 Decomposing text into segments and links. A link connects two adjacent segments. Tian et al. [30] WeText ✕ ✓ ✕ ✕ ICCV 2017 Proposing a weakly supervised scene text detection method (WeText). Zhu et al. [31] RTN ✕ ✓ ✕ ✕ ICDAR 2017 Mainly basing CTPN vertical vertical proposal mechanism. Ren et al. [34] ✕ ✓ ✕ ✕ TMM 2017 Proposing a CNN-based detector. It contains a text structure component detector layer, a spatial pyramid layer, and a multi-input-layer deep belief network (DBN). Zhang et al. [10] ✕ ✓ ✕ ✕ CVPR 2015 The proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images. Wang et al. [86] DSRN ✕ ✓ ✓ ✕ IJCAI 2019 Presenting a scale-transfer module and scale relationship module to handle the problem of scale variation. Tang et al.[89] Seglink++ ✕ ✓ ✓ ✓ PR 2019 Presenting instance aware component grouping (ICG) for arbitrary-shape text detection. Wang et al.[92] ContourNet ✓ ✓ ✓ ✓ CVPR 2020 1.A scale-insensitive Adaptive Region Proposal Network (AdaptiveRPN); 2. Local Orthogonal Texture-aware Module (LOTM). 2.1.4 Hybrid Methods Method Model Code Hori Quad Irreg Source Time Highlight Tang et al. [52] SSFT ✕ ✓ ✕ ✕ TMM 2018 Proposing a novel scene text detection method that involves superpixel-based stroke feature transform (SSFT) and deep learning based region classification (DLRC). Xie et al.[61] SPCNet ✕ ✓ ✓ ✓ AAAI 2019 Text Context module + Re-Score mechanism. Liu et al. [64] PMTD ✓ ✓ ✓ ✕ arXiv 2019 Perform “soft” semantic segmentation. It assigns a soft pyramid label (i.e., a real value between 0 and 1) for each pixel within text instance. Liu et al. [80] BDN ✓ ✓ ✓ ✕ IJCAI 2019 Discretizing bouding boxes into key edges to address label confusion for text detection. Zhang et al. [81] LOMO ✕ ✓ ✓ ✓ CVPR 2019 DR + IRM + SEM Zhou et al. [24] EAST ✓ ✓ ✓ ✕ CVPR 2017 The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images with instance segmentation. Yue et al. [48] ✕ ✓ ✓ ✕ BMVC 2018 Proposing a general framework for text detection called Guided CNN to achieve the two goals simultaneously. Zhong et al. [53] AF-RPN ✕ ✓ ✓ ✕ arXiv 2018 Presenting AF-RPN(anchor-free) as an anchor-free and scale-friendly region proposal network for the Faster R-CNN framework. Xue et al.[85] MSR ✕ ✓ ✓ ✓ IJCAI 2019 Presenting a noval multi-scale regression network. Liao et al. [91] DB ✓ ✓ ✓ ✓ AAAI 2020 Presenting differentiable binarization module to adaptively set the thresholds for binarization, which simplifies the post-processing. Xiao et al. [93] SDM ✕ ✓ ✓ ✓ ECCV 2020 1. A novel sequential deformation method; 2. auxiliary character counting supervision. 2.2 Detection Results2.2.1 Detection Results on Horizontal-Text Datasets Method Model Source Time Method Category IC11[68] IC13 [69] IC05[67] P R F P R F P R F Yao et al. [1] TD-Mixture CVPR 2012 Traditional ~ ~ ~ 0.69 0.66 0.67 ~ ~ ~ Yin et al. [2] TPAMI 2013 0.86 0.68 0.76 ~ ~ ~ ~ ~ ~ Yin et al. [7] TPAMI 2015 0.838 0.66 0.738 ~ ~ ~ ~ ~ ~ Wu et al. [9] TMM 2015 ~ ~ ~ 0.76 0.70 0.73 ~ ~ ~ Liang et al. [8] TIP 2015 0.77 0.68 0.71 0.76 0.68 0.72 ~ ~ ~ Michal et al.[12] FASText ICCV 2015 ~ ~ ~ 0.84 0.69 0.77 ~ ~ ~ Li et al. [3] TIP 2014 Segmentation 0.80 0.62 0.70 ~ ~ ~ ~ ~ ~ Zhang et al. [14] CVPR 2016 ~ ~ ~ 0.88 0.78 0.83 ~ ~ ~ He et al. [18] Text-CNN TIP 2016 0.91 0.74 0.82 0.93 0.73 0.82 0.87 0.73 0.79 Yao et al. [21] arXiv 2016 ~ ~ ~ 0.889 0.802 0.843 ~ ~ ~ Hu et al. [27] WordSup ICCV 2017 ~ ~ ~ 0.933 0.875 0.903 ~ ~ ~ Tang et al.[32] TIP 2017 0.90 0.86 0.88 0.92 0.87 0.89 ~ ~ ~ Wang et al. [38] ICDAR 2017 0.87 0.78 0.82 0.87 0.82 0.84 ~ ~ ~ Deng et al. [40] PixelLink AAAI 2018 ~ ~ ~ 0.886 0.875 0.881 ~ ~ ~ Liu et al. [42] MCN CVPR 2018 ~ ~ ~ 0.88 0.87 0.88 ~ ~ ~ Lyu et al. [43] CVPR 2018 ~ ~ ~ 0.92 0.844 0.880 ~ ~ ~ Chu et al. [45] Border ECCV 2018 ~ ~ ~ 0.915 0.871 0.892 ~ ~ ~ Wang et al. [54] PSENet CVPR 2019 ~ ~ ~ 0.94 0.90 0.92 ~ ~ ~ Huang et al. [4] MSERs-CNN ECCV 2014 0.88 0.71 0.78 ~ ~ ~ 0.84 0.67 0.75 Sun et al. [6] PR 2015 0.92 0.91 0.91 0.94 0.92 0.93 ~ ~ ~ Gupta et al. [15] FCRN CVPR 2016 Regression 0.94 0.77 0.85 0.938 0.764 0.842 ~ ~ ~ Zhong et al. [20] DeepText arXiv 2016 0.87 0.83 0.85 0.85 0.81 0.83 ~ ~ ~ Liao et al. [22] TextBoxes AAAI 2017 0.89 0.82 0.86 0.89 0.83 0.86 ~ ~ ~ Liu et al. [25] DMPNet CVPR 2017 ~ ~ ~ 0.93 0.83 0.870 ~ ~ ~ Jiang et al. [36] R2CNN arXiv 2017 ~ ~ ~ 0.92 0.81 0.86 ~ ~ ~ Xing et al. [37] ArbiText arXiv 2017 ~ ~ ~ 0.826 0.936 0.877 ~ ~ ~ Wang et al. [41] ITN CVPR 2018 0.896 0.889 0.892 0.941 0.893 0.916 ~ ~ ~ Liao et al. [49] TextBoxes++ TIP 2018 ~ ~ ~ 0.92 0.86 0.89 ~ ~ ~ He et al. [50] TIP 2018 ~ ~ ~ 0.91 0.84 0.88 ~ ~ ~ Ma et al. [51] RRPN TMM 2018 ~ ~ ~ 0.95 0.89 0.91 ~ ~ ~ Zhu et al. [55] SLPR arXiv 2018 ~ ~ ~ 0.90 0.72 0.80 ~ ~ ~ Cai et al. [59] FFN ICIP 2018 ~ ~ ~ 0.92 0.84 0.876 ~ ~ ~ Sabyasachi et al. [60] RGC ICIP 2018 ~ ~ ~ 0.89 0.77 0.83 ~ ~ ~ Wang et al. [82] CVPR 2019 ~ ~ ~ 0.937 0.878 0.907 ~ ~ ~ Liu et al. [84] CVPR 2019 ~ ~ ~ 0.937 0.897 0.917 ~ ~ ~ He et al. [29] SSTD ICCV 2017 ~ ~ ~ 0.89 0.86 0.88 ~ ~ ~ Tian et al. [11] ICCV 2015 0.86 0.76 0.81 0.852 0.759 0.802 ~ ~ ~ Tian et al. [13] CTPN ECCV 2016 ~ ~ ~ 0.93 0.83 0.88 ~ ~ ~ He et al. [19] ACCV 2016 ~ ~ ~ 0.90 0.75 0.81 ~ ~ ~ Shi et al. [23] SegLink CVPR 2017 ~ ~ ~ 0.877 0.83 0.853 ~ ~ ~ Tian et al. [30] WeText ICCV 2017 ~ ~ ~ 0.911 0.831 0.869 ~ ~ ~ Zhu et al. [31] RTN ICDAR 2017 ~ ~ ~ 0.94 0.89 0.91 ~ ~ ~ Ren et al. [34] TMM 2017 0.78 0.67 0.72 0.81 0.67 0.73 ~ ~ ~ Zhang et al. [10] CVPR 2015 0.84 0.76 0.80 0.88 0.74 0.80 ~ ~ ~ Tang et al. [52] SSFT TMM 2018 Hybrid 0.906 0.847 0.876 0.911 0.861 0.885 ~ ~ ~ Xie et al.[61] SPCNet AAAI 2019 ~ ~ ~ 0.94 0.91 0.92 ~ ~ ~ Liu et al. [80] BDN IJCAI 2019 ~ ~ ~ 0.887 0.894 0.89 ~ ~ ~ Zhou et al. [24] EAST CVPR 2017 ~ ~ ~ 0.93 0.83 0.870 ~ ~ ~ Yue et al. [48] BMVC 2018 ~ ~ ~ 0.885 0.846 0.870 ~ ~ ~ Zhong et al. [53] AF-RPN arXiv 2018 ~ ~ ~ 0.94 0.90 0.92 ~ ~ ~ Xue et al.[85] MSR IJCAI 2019 ~ ~ ~ 0.918 0.885 0.901 ~ ~ ~ 2.2.2 Detection Results on Arbitrary-Quadrilateral-Text Datasets Method Model Source Time Method Category IC15 [70] MSRA-TD500 [71] USTB-SV1K [65] SVT [66] P R F P R F P R F P R F Le et al. [5] HOCC CVPR 2014 Traditional ~ ~ ~ 0.71 0.62 0.66 ~ ~ ~ ~ ~ ~ Yin et al. [7] TPAMI 2015 ~ ~ ~ 0.81 0.63 0.71 0.499 0.454 0.475 ~ ~ ~ Wu et al. [9] TMM 2015 ~ ~ ~ 0.63 0.70 0.66 ~ ~ ~ ~ ~ ~ Tian et al. [17] IJCAI 2016 ~ ~ ~ 0.95 0.58 0.721 0.537 0.488 0.51 ~ ~ ~ Yang et al. [33] TIP 2017 ~ ~ ~ 0.95 0.58 0.72 0.54 0.49 0.51 ~ ~ ~ Liang et al. [8] TIP 2015 ~ ~ ~ 0.74 0.66 0.70 ~ ~ ~ ~ ~ ~ Zhang et al. [14] CVPR 2016 Segmentation 0.71 0.43 0.54 0.83 0.67 0.74 ~ ~ ~ ~ ~ ~ Zhu et al. [16] CVPR 2016 0.81 0.91 0.85 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [18] Text-CNN TIP 2016 ~ ~ ~ 0.76 0.61 0.69 ~ ~ ~ ~ ~ ~ Yao et al. [21] arXiv 2016 0.723 0.587 0.648 0.765 0.753 0.759 ~ ~ ~ ~ ~ ~ Hu et al. [27] WordSup ICCV 2017 0.793 0.77 0.782 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wu et al. [28] ICCV 2017 0.91 0.78 0.84 0.77 0.78 0.77 ~ ~ ~ ~ ~ ~ Dai et al. [35] FTSN arXiv 2017 0.886 0.80 0.841 0.876 0.771 0.82 ~ ~ ~ ~ ~ ~ Deng et al. [40] PixelLink AAAI 2018 0.855 0.820 0.837 0.830 0.732 0.778 ~ ~ ~ ~ ~ ~ Liu et al. [42] MCN CVPR 2018 0.72 0.80 0.76 0.88 0.79 0.83 ~ ~ ~ ~ ~ ~ Lyu et al. [43] CVPR 2018 0.895 0.797 0.843 0.876 0.762 0.815 ~ ~ ~ ~ ~ ~ Chu et al. [45] Border ECCV 2018 ~ ~ ~ 0.830 0.774 0.801 ~ ~ ~ ~ ~ ~ Long et al. [46] TextSnake ECCV 2018 0.849 0.804 0.826 0.832 0.739 0.783 ~ ~ ~ ~ ~ ~ Yang et al. [47] IncepText IJCAI 2018 0.938 0.873 0.905 0.875 0.790 0.830 ~ ~ ~ ~ ~ ~ Wang et al. [54] PSENet CVPR 2019 0.8692 0.845 0.8569 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xu et al.[57] TextField arXiv 2018 0.843 0.805 0.824 0.874 0.759 0.813 ~ ~ ~ ~ ~ ~ Tian et al. [58] FTDN ICIP 2018 0.847 0.773 0.809 ~ ~ ~ ~ ~ ~ ~ ~ ~ Tian et al. [83] CVPR 2019 0.883 0.850 0.866 0.842 0.817 0.829 ~ ~ ~ ~ ~ ~ Baek et al. [62] CRAFT CVPR 2019 0.898 0.843 0.869 0.882 0.782 0.829 ~ ~ ~ ~ ~ ~ Richardson et al. [87] IJCAI 2019 0.853 0.83 0.827 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wang et al. [88] SAST ACMM 2019 0.8755 0.8734 0.8744 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wang et al. [90] PAN ICCV 2019 0.84 0.819 0.829 0.844 0.838 0.821 ~ ~ ~ ~ ~ ~ Gupta et al. [15] FCRN CVPR 2016 Regression ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.651 0.599 0.624 Liu et al. [25] DMPNet CVPR 2017 0.732 0.682 0.706 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [26] DDR ICCV 2017 0.82 0.80 0.81 0.77 0.70 0.74 ~ ~ ~ ~ ~ ~ Jiang et al. [36] R2CNN arXiv 2017 0.856 0.797 0.825 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xing et al. [37] ArbiText arXiv 2017 0.792 0.735 0.759 0.78 0.72 0.75 ~ ~ ~ ~ ~ ~ Wang et al. [41] ITN CVPR 2018 0.857 0.741 0.795 0.903 0.723 0.803 ~ ~ ~ ~ ~ ~ Liao et al. [44] RRD CVPR 2018 0.88 0.8 0.838 0.876 0.73 0.79 ~ ~ ~ ~ ~ ~ Liao et al. [49] TextBoxes++ TIP 2018 0.878 0.785 0.829 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [50] TIP 2018 0.85 0.80 0.82 0.91 0.81 0.86 ~ ~ ~ ~ ~ ~ Ma et al. [51] RRPN TMM 2018 0.822 0.732 0.774 0.821 0.677 0.742 ~ ~ ~ ~ ~ ~ Zhu et al. [55] SLPR arXiv 2018 0.855 0.836 0.845 ~ ~ ~ ~ ~ ~ ~ ~ ~ Deng et al. [56] arXiv 2018 0.89 0.81 0.845 ~ ~ ~ ~ ~ ~ ~ ~ ~ Sabyasachi et al. [60] RGC ICIP 2018 0.83 0.81 0.82 0.85 0.76 0.80 ~ ~ ~ ~ ~ ~ Wang et al. [82] CVPR 2019 0.892 0.86 0.876 0.852 0.821 0.836 ~ ~ ~ ~ ~ ~ He et al. [29] SSTD ICCV 2017 0.80 0.73 0.77 ~ ~ ~ ~ ~ ~ ~ ~ ~ Tian et al. [13] CTPN ECCV 2016 0.74 0.52 0.61 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [19] ACCV 2016 ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.87 0.73 0.79 Shi et al. [23] SegLink CVPR 2017 0.731 0.768 0.75 0.86 0.70 0.77 ~ ~ ~ ~ ~ ~ Wang et al. [86] DSRN IJCAI 2019 0.832 0.796 0.814 0.876 0.712 0.785 ~ ~ ~ ~ ~ ~ Tang et al.[89] Seglink++ PR 2019 0.837 0.803 0.820 ~ ~ ~ ~ ~ ~ ~ ~ ~ Wang et al. [92] ContourNet CVPR 2020 0.876 0.861 0.869 ~ ~ ~ ~ ~ ~ ~ ~ ~ Tang et al. [52] SSFT TMM 2018 Hybrid ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.541 0.758 0.631 Xie et al.[61] SPCNet AAAI 2019 0.89 0.86 0.87 ~ ~ ~ ~ ~ ~ ~ ~ ~ Liu et al. [64] PMTD arXiv 2019 0.913 0.874 0.893 ~ ~ ~ ~ ~ ~ ~ ~ ~ Liu et al. [80] BDN IJCAI 2019 0.881 0.846 0.863 0.87 0.815 0.842 ~ ~ ~ ~ ~ ~ Zhang et al. [81] LOMO CVPR 2019 0.878 0.876 0.877 ~ ~ ~ ~ ~ ~ ~ ~ ~ Zhou et al. [24] EAST CVPR 2017 0.833 0.783 0.807 0.873 0.674 0.761 ~ ~ ~ ~ ~ ~ Yue et al. [48] BMVC 2018 0.866 0.789 0.823 ~ ~ ~ ~ ~ ~ 0.691 0.660 0.675 Zhong et al. [53] AF-RPN arXiv 2018 0.89 0.83 0.86 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xue et al.[85] MSR IJCAI 2019 ~ ~ ~ 0.874 0.767 0.817 ~ ~ ~ ~ ~ ~ Liao et al. [91] DB AAAI 2020 0.918 0.832 0.873 0.915 0.792 0.849 ~ ~ ~ ~ ~ ~ Xiao et al. [93] SDM ECCV 2020 0.9196 0.8922 0.9057 ~ ~ ~ ~ ~ ~ ~ ~ ~ Method Model Source Time Method Category IC15 [70] MSRA-TD500 [71] USTB-SV1K [65] SVT [66] P R F P R F P R F P R F Le et al. [5] HOCC CVPR 2014 Traditional ~ ~ ~ ~ ~ ~ ~ ~ ~ 0.80 0.73 0.76 Yao et al. [21] arXiv 2016 Segmentation 0.432 0.27 0.333 ~ ~ ~ ~ ~ ~ ~ ~ ~ Hu et al. [27] WordSup ICCV 2017 0.452 0.309 0.368 ~ ~ ~ ~ ~ ~ ~ ~ ~ Lyu et al. [43] CVPR 2018 0.351 0.348 0.349 ~ ~ ~ 0.743 0.706 0.724 ~ ~ ~ Chu et al. [45] Border ECCV 2018 ~ ~ ~ 0.782 0.588 0.671 0.777 0.621 0.690 ~ ~ ~ Yang et al. [47] IncepText IJCAI 2018 ~ ~ ~ 0.785 0.569 0.660 ~ ~ ~ ~ ~ ~ Wang et al. [54] PSENet CVPR 2019 ~ ~ ~ ~ ~ ~ 0.7535 0.6918 0.7213 ~ ~ ~ Baek et al. [62] CRAFT CVPR 2019 ~ ~ ~ ~ ~ ~ 0.806 0.682 0.739 ~ ~ ~ He et al. [29] SSTD ICCV 2017 Regression 0.46 0.31 0.37 ~ ~ ~ ~ ~ ~ ~ ~ ~ Gupta et al. [15] FCRN CVPR 2016 ~ ~ ~ ~ ~ ~ 0.844 0.763 0.801 ~ ~ ~ Liao et al. [49] TextBoxes++ TIP 2018 0.61 0.57 0.59 ~ ~ ~ ~ ~ ~ ~ ~ ~ Ma et al. [51] RRPN TMM 2018 ~ ~ ~ ~ ~ ~ 0.7669 0.5794 0.6601 ~ ~ ~ Deng et al. [56] arXiv 2018 0.555 0.633 0.591 ~ ~ ~ ~ ~ ~ ~ ~ ~ Cai et al. [59] FFN ICIP 2018 0.43 0.35 0.39 ~ ~ ~ ~ ~ ~ ~ ~ ~ Xie et al. [79] DeRPN AAAI 2019 0.586 0.557 0.571 ~ ~ ~ ~ ~ ~ ~ ~ ~ He et al. [29] SSTD ICCV 2017 0.46 0.31 0.37 ~ ~ ~ ~ ~ ~ ~ ~ ~ Liao et al. [44] RRD CVPR 2018 ~ ~ ~ 0.591 0.775 0.670 ~ ~ ~ ~ ~ ~ Richardson et al. [87] IJCAI 2019 ~ ~ ~ ~ ~ ~ 0.729 0.618 0.669 ~ ~ ~ Wang et al. [88] SAST ACMM 2019 ~ ~ ~ ~ ~ ~ 0.7935 0.6653 0.7237 ~ ~ ~ Xie et al.[61] SPCNet AAAI 2019 Hybrid ~ ~ ~ ~ ~ ~ 0.806 0.686 0.741 ~ ~ ~ Liu et al. [64] PMTD arXiv 2019 ~ ~ ~ ~ ~ ~ 0.844 0.763 0.801 ~ ~ ~ Liu et al. [80] BDN IJCAI 2019 ~ ~ ~ ~ ~ ~ 0.791 0.698 0.742 ~ ~ ~ Zhang et al. [81] LOMO CVPR 2019 ~ ~ ~ 0.791 0.602 0.684 0.802 0.672 0.731 ~ ~ ~ Zhou et al. [24] EAST CVPR 2017 0.504 0.324 0.395 ~ ~ ~ ~ ~ ~ ~ ~ ~ Zhong et al. [53] AF-RPN arXiv 2018 ~ ~ ~ ~ ~ ~ 0.75 0.66 0.70 ~ ~ ~ Liao et al. [91] DB AAAI 2020 ~ ~ ~ ~ ~ ~ 0.831 0.679 0.747 ~ ~ ~ Xiao et al. [93] SDM ECCV 2020 ~ ~ ~ ~ ~ ~ 0.8679 0.7526 0.8061 ~ ~ ~ 2.2.3 Detection Results on Irregular-Text DatasetsIn this section, we only select those methods suitable for irregular text detection. Method Model Source Time Method Category Total-text [74] SCUT-CTW1500 [75] P R F P R F Baek et al. [62] CRAFT CVPR 2019 Segmentation 0.876 0.799 0.836 0.860 0.811 0.835 Long et al. [46] TextSnake ECCV 2018 0.827 0.745 0.784 0.679 0.853 0.756 Tian et al. [83] CVPR 2019 ~ ~ ~ 81.7 84.2 80.1 Wang et al. [54] PSENet CVPR 2019 0.840 0.779 0.809 0.848 0.797 0.822 Wang et al. [88] SAST ACMM 2019 0.8557 0.7549 0.802 0.8119 0.8171 0.8145 Wang et al. [90] PAN ICCV 2019 0.893 0.81 0.85 0.864 0.812 0.837 Zhu et al. [55] SLPR arXiv 2018 Regression ~ ~ ~ 0.801 0.701 0.748 Liu et al. [63] CTD+TLOC PR 2019 ~ ~ ~ 0.774 0.698 0.734 Wang et al. [82] CVPR 2019 ~ ~ ~ 80.1 80.2 80.1 Liu et al. [84] CVPR 2019 0.814 0.791 0.802 0.787 0.761 0.774 Tang et al.[89] Seglink++ PR 2019 0.829 0.809 0.815 0.828 0.798 0.813 Wang et al. [92] ContourNet CVPR 2020 0.869 0.839 0.854 0.837 0.841 0.839 Zhang et al. [81] LOMO CVPR 2019 Hybrid 0.876 0.793 0.833 0.857 0.765 0.808 Xie et al.[61] SPCNet AAAI 2019 0.83 0.83 0.83 ~ ~ ~ Xue et al.[85] MSR IJCAI 2019 0.852 0.73 0.768 0.838 0.778 0.807 Liao et al. [91] DB AAAI 2020 0.871 0.825 0.847 0.869 0.802 0.834 Xiao et al.[93] SDM ECCV 2020 0.9085 0.8603 0.8837 0.884 0.8442 0.8636 3. Survey[A] [TPAMI-2015] Ye Q, Doermann D. Text detection and recognition in imagery: A survey[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(7): 1480-1500. paper[B] [Frontiers-Comput. Sci-2016] Zhu Y, Yao C, Bai X. Scene text detection and recognition: Recent advances and future trends[J]. Frontiers of Computer Science, 2016, 10(1): 19-36. paper[C] [arXiv-2018] Long S, He X, Ya C. Scene Text Detection and Recognition: The Deep Learning Era[J]. arXiv preprint arXiv:1811.04256, 2018. paper4. EvaluationIf you are insterested in developing better scene text detection metrics, some references recommended here might be useful.[A] Wolf, Christian, and Jean-Michel Jolion. "Object count/area graphs for the evaluation of object detection and segmentation algorithms." International Journal of Document Analysis and Recognition (IJDAR) 8.4 (2006): 280-296. paper[B] D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. K. Ghosh, A. D.Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu, F. Shafait, S. Uchida, and E. Valveny. ICDAR 2015 competition on robust reading. In ICDAR, pages 1156–1160, 2015. paper[C] Calarasanu, Stefania, Jonathan Fabrizio, and Severine Dubuisson. "What is a good evaluation protocol for text localization systems? Concerns, arguments, comparisons and solutions." Image and Vision Computing 46 (2016): 1-17. paper[D] Shi, Baoguang, et al. "ICDAR2017 competition on reading chinese text in the wild (RCTW-17)." 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). Vol. 1. IEEE, 2017. paper[E] Nayef, N; Yin, F; Bizid, I; et al. ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-rrc-mlt. In Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, volume 1, 1454–1459. IEEE.paper[F] Dangla, Aliona, et al. "A first step toward a fair comparison of evaluation protocols for text detection algorithms." 2018 13th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2018. paper[G] He,Mengchao and Liu, Yuliang, et al. ICPR2018 Contest on Robust Reading for Multi-Type Web images. ICPR 2018. paper[H] Liu, Yuliang and Jin, Lianwen, et al. "Tightness-aware Evaluation Protocol for Scene Text Detection" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019. paper code5. OCR ServiceOCRAPIFreeTesseract OCR Engine×√Azure√√ABBYY√√OCR Space√√SODA PDF OCR√√Free Online OCR√√Online OCR√√Super Tools√√Online Chinese Recognition√√Calamari OCR×√Tencent OCR√×6. References and Code [1] Yao C, Bai X, Liu W, et al. 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The IEEE International Conference on Computer Vision (ICCV). 2017, 6(7). Paper Code[30] Tian S, Lu S, Li C. Wetext: Scene text detection under weak supervision. ICCV, 2017. Paper[31] Zhu, Xiangyu and Jiang, Yingying et al. Deep Residual Text Detection Network for Scene Text. ICDAR, 2017. Paper[32] Tang Y , Wu X. Scene Text Detection and Segmentation Based on Cascaded Convolution Neural Networks. IEEE Transactions on Image Processing, 2017, 26(3):1509-1520. Paper[33] Yang C, Yin X C, Pei W Y, et al. Tracking Based Multi-Orientation Scene Text Detection: A Unified Framework with Dynamic Programming. IEEE Transactions on Image Processing, 2017. Paper[34] X. Ren, Y. Zhou, J. He, K. Chen, X. Yang and J. Sun, A Convolutional Neural Network-Based Chinese Text Detection Algorithm via Text Structure Modeling. in IEEE Transactions on Multimedia, vol. 19, no. 3, pp. 506-518, March 2017. Paper[35] Dai Y, Huang Z, Gao Y, et al. Fused text segmentation networks for multi-oriented scene text detection. arXiv preprint arXiv:1709.03272, 2017. Paper[36] Jiang Y, Zhu X, Wang X, et al. R2CNN: rotational region CNN for orientation robust scene text detection. arXiv preprint arXiv:1706.09579, 2017. Paper[37] Xing D, Li Z, Chen X, et al. ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene. arXiv preprint arXiv:1711.11249, 2017. Paper[38] C. Wang, F. Yin and C. Liu, Scene Text Detection with Novel Superpixel Based Character Candidate Extraction. in 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017, pp. 929-934. Paper[39] Sheng Zhang, Yuliang Liu, Lianwen Jin et al. Feature Enhancement Network: A Refined Scene Text Detector. In AAAI 2018. Paper[40] Dan Deng et al. PixelLink: Detecting Scene Text via Instance Segmentation. In AAAI 2018. Paper Code[41] Fangfang Wang, Liming Zhao, Xi L et al. Geometry-Aware Scene Text Detection with Instance Transformation Network. In CVPR 2018. Paper[42] Zichuan Liu, Guosheng Lin, Sheng Yang et al. Learning Markov Clustering Networks for Scene Text Detection. In CVPR 2018. Paper[43] Pengyuan Lyu, Cong Yao, Wenhao Wu et al. Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation. In CVPR 2018. Paper[44] Minghui L, Zhen Z, Baoguang S. Rotation-Sensitive Regression for Oriented Scene Text Detection. In CVPR 2018. Paper[45] Chuhui Xue et al. Accurate Scene Text Detection through Border Semantics Awareness and Bootstrapping. In ECCV 2018. Paper[46] Long, Shangbang and Ruan, Jiaqiang, et al. TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes. In ECCV, 2018. Paper[47] Qiangpeng Yang, Mengli Cheng et al. IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection. In IJCAI 2018. Paper[48] Xiaoyu Yue et al. Boosting up Scene Text Detectors with Guided CNN. In BMVC 2018. Paper[49] Liao M, Shi B , Bai X. TextBoxes++: A Single-Shot Oriented Scene Text Detector. IEEE Transactions on Image Processing, 2018, 27(8):3676-3690. Paper Code[50] W. He, X. Zhang, F. Yin and C. Liu, Multi-Oriented and Multi-Lingual Scene Text Detection With Direct Regression, in IEEE Transactions on Image Processing, vol. 27, no. 11, pp.5406-5419, 2018. Paper[51] Ma J, Shao W, Ye H, et al. Arbitrary-oriented scene text detection via rotation proposals.in IEEE Transactions on Multimedia, 2018. Paper Code[52] Youbao Tang and Xiangqian Wu. Scene Text Detection Using Superpixel-Based Stroke Feature Transform and Deep Learning Based Region Classification. In TMM, 2018. Paper[53] Zhuoyao Zhong, Lei Sun and Qiang Huo. An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches. arXiv preprint arXiv:1804.09003. 2018. Paper[54] Wenhai W, Enze X, et al. Shape Robust Text Detection with Progressive Scale Expansion Network. In CVPR 2019. Paper Code[55] Zhu Y, Du J. 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Paper[63] Yuliang L, Lianwen J, Shuaitao Z, et al. Curved Scene Text Detection via Transverse and Longitudinal Sequence Connection. Pattern Recognition, 2019. Paper Code[64] Jingchao Liu, Xuebo Liu, et al, Pyramid Mask Text Detector. arXiv preprint arXiv:1903.11800, 2019. Paper Code[79] Lele Xie, Yuliang Liu, Lianwen Jin, Zecheng Xie, DeRPN: Taking a further step toward more general object detection. In AAAI, 2019. Paper Code[80] Yuliang Liu, Lianwen Jin, et al, Omnidirectional Scene Text Detction with Sequential-free Box Discretization. In IJCAI, 2019.Paper Code[81] Chengquan Zhang, Borong Liang, et al, Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes. In CVPR, 2019.Paper[82] Xiaobing Wang, Yingying Jiang, et al, Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation. In CVPR, 2019. Paper[83] Zhuotao Tian, Michelle Shu, et al, Learning Shape-Aware Embedding for Scene Text Detection. In CVPR, 2019. Paper[84] Zichuan Liu, Guosheng Lin, et al, Towards Robust Curve Text Detection with Conditional Spatial Expansion. In CVPR, 2019. Paper[85] Xue C, Lu S, Zhang W. MSR: multi-scale shape regression for scene text detection. In IJCAI, 2019. Paper[86] Wang Y, Xie H, Fu Z, et al. DSRN: a deep scale relationship network for scene text detection. In IJCAI, 2019: 947-953. Paper[87] Elad Richardson, et al, It's All About The Scale -- Efficient Text Detection Using Adaptive Scaling. In WACV, 2020. Paper[88] Pengfei Wang, et al, A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning. In ACMM, 2019. Paper[89] Jun Tang, et al, SegLink ++: Detecting Dense and Arbitrary-shaped Scene Text by Instance-aware Component Grouping. In PR, 2019. Paper[90] Wenhai Wang, et al, Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network. In ICCV, 2019. Paper[91] Minghui Liao, et al, Real-time Scene Text Detection with Differentiable Binarization. In AAAI, 2020. PaperCode[92] Wang, Yuxin, et al. ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection. CVPR. 2020. PaperCode[93] Xiao, et al, Sequential Deformation for Accurate Scene Text Detection. In ECCV, 2020. Paper DatasetsUSTB-SV1K[65]：Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, and Hong-Wei Hao, Robust text detection in natural scene images, IEEE Trans. Pattern Analysis and Machine Intelligence (TPAMI), priprint, 2013. PaperSVT[66]: Wang,Kai, and S. Belongie. Word Spotting in the Wild. European Conference on Computer Vision(ECCV), 2010: 591-604. PaperICDAR2005[67]: Lucas, S: ICDAR 2005 text locating competition results. In: ICDAR ,2005. PaperICDAR2011[68]: Shahab, A, Shafait, F, Dengel, A: ICDAR 2011 robust reading competition challenge 2: Reading text in scene images. In: ICDAR, 2011. PaperICDAR2013[69]：D. Karatzas, F. Shafait, S. Uchida, et al. ICDAR 2013 robust reading competition. In ICDAR, 2013. PaperICDAR2015[70]：D. Karatzas, L. Gomez-Bigorda, A. Nicolaou, S. K. Ghosh, A. D.Bagdanov, M. Iwamura, J. Matas, L. Neumann, V. R. Chandrasekhar, S. Lu, F. Shafait, S. Uchida, and E. Valveny. ICDAR 2015 competition on robust reading. In ICDAR, pages 1156–1160, 2015. PaperMSRA-TD500[71]：C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu, Detecting texts of arbitrary orientations in natural images. in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2012, pp.1083–1090.PaperCOCO-Text[72]：Veit A, Matera T, Neumann L, et al. Coco-text: Dataset and benchmark for text detection and recognition in natural images. arXiv preprint arXiv:1601.07140, 2016. PaperRCTW-17[73]：Shi B, Yao C, Liao M, et al. ICDAR2017 competition on reading chinese text in the wild (RCTW-17). Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, 2017, 1: 1429-1434. PaperTotal-Text[74]：Chee C K, Chan C S. Total-text: A comprehensive dataset for scene text detection and recognition.Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on. IEEE, 2017, 1: 935-942.PaperSCUT-CTW1500[75]：Yuliang L, Lianwen J, Shuaitao Z, et al. Curved Scene Text Detection via Transverse and Longitudinal Sequence Connection. Pattern Recognition, 2019.PaperMLT 2017[76]: Nayef, N; Yin, F; Bizid, I; et al. ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-rrc-mlt. In Document Analysis and Recognition (ICDAR), 2017 14th IAPR International Conference on, volume 1, 1454–1459. IEEE. PaperOSTD[77]: Chucai Yi and YingLi Tian, Text string detection from natural scenes by structure-based partition and grouping, In IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2594–2605, 2011. PaperCTW[78]: Yuan T L, Zhu Z, Xu K, et al. Chinese Text in the Wild. arXiv preprint arXiv:1803.00085, 2018. PaperIf you find any problems in our resources, or any good papers/codes we have missed, please inform us at liuchongyu1996@gmail.com. Thank you for your contribution.CopyrightCopyright © 2019 SCUT-DLVC. All Rights Reserved.
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ubuntu安装和配置aria2 ubuntu安装和配置aria2安装 sudo apt-get install aria2配置sudo mkdir /etc/aria2 #新建文件夹 sudo touch /etc/aria2/aria2.session #新建session文件 sudo chmod 777 /etc/aria2/aria2.session #设置aria2.session可写 sudo vim /etc/aria2/aria2.conf #创建配置文件配置文件内容# 文件的保存路径(可使用绝对路径或相对路径), 默认: 当前启动位置 dir=/mnt/sdb1/download # 启用磁盘缓存, 0为禁用缓存, 需1.16以上版本, 默认:16M disk-cache=32M # 文件预分配方式, 能有效降低磁盘碎片, 默认:prealloc # 预分配所需时间: none < falloc ? trunc < prealloc # falloc和trunc则需要文件系统和内核支持 # NTFS建议使用falloc, EXT3/4建议trunc, MAC 下需要注释此项 file-allocation=trunc # 断点续传 continue=true ## 下载连接相关 ## # 最大同时下载任务数, 运行时可修改, 默认:5 max-concurrent-downloads=10 # 同一服务器连接数, 添加时可指定, 默认:1 max-connection-per-server=16 # 最小文件分片大小, 添加时可指定, 取值范围1M -1024M, 默认:20M # 假定size=10M, 文件为20MiB 则使用两个来源下载; 文件为15MiB 则使用一个来源下载 min-split-size=10M # 单个任务最大线程数, 添加时可指定, 默认:5 split=16 # 整体下载速度限制, 运行时可修改, 默认:0 max-overall-download-limit=0 # 单个任务下载速度限制, 默认:0 #max-download-limit=0 # 整体上传速度限制, 运行时可修改, 默认:0 max-overall-upload-limit=80 # 单个任务上传速度限制, 默认:0 #max-upload-limit=1000 # 禁用IPv6, 默认:false disable-ipv6=false #检查证书 check-certificate=false ## 进度保存相关 ## # 从会话文件中读取下载任务 input-file=/etc/aria2/aria2.session # 在Aria2退出时保存`错误/未完成`的下载任务到会话文件 save-session=/etc/aria2/aria2.session # 定时保存会话, 0为退出时才保存, 需1.16.1以上版本, 默认:0 #save-session-interval=60 ## RPC相关设置 ## # 启用RPC, 默认:false enable-rpc=true # 允许所有来源, 默认:false rpc-allow-origin-all=true # 允许非外部访问, 默认:false rpc-listen-all=true # 事件轮询方式, 取值:[epoll, kqueue, port, poll, select], 不同系统默认值不同 #event-poll=select # RPC监听端口, 端口被占用时可以修改, 默认:6800 rpc-listen-port=6800 # 设置的RPC授权令牌, v1.18.4新增功能, 取代 --rpc-user 和 --rpc-passwd 选项 rpc-secret=123456 # 设置的RPC访问用户名, 此选项新版已废弃, 建议改用 --rpc-secret 选项 #rpc-user=<USER> # 设置的RPC访问密码, 此选项新版已废弃, 建议改用 --rpc-secret 选项 #rpc-passwd=<PASSWD> ## BT/PT下载相关 ## # 当下载的是一个种子(以.torrent结尾)时, 自动开始BT任务, 默认:true follow-torrent=true # BT监听端口, 当端口被屏蔽时使用, 默认:6881-6999 #listen-port=51413 # 单个种子最大连接数, 默认:55 #bt-max-peers=55 # 打开DHT功能, PT需要禁用, 默认:true enable-dht=true # 打开IPv6 DHT功能, PT需要禁用 #enable-dht6=false # DHT网络监听端口, 默认:6881-6999 #dht-listen-port=6881-6999 # 本地节点查找, PT需要禁用, 默认:false #bt-enable-lpd=true # 种子交换, PT需要禁用, 默认:true enable-peer-exchange=true # 每个种子限速, 对少种的PT很有用, 默认:50K #bt-request-peer-speed-limit=50K # 客户端伪装, PT需要 peer-id-prefix=-TR2770- user-agent=Transmission/2.77 # 当种子的分享率达到这个数时, 自动停止做种, 0为一直做种, 默认:1.0 seed-ratio=0.1 # 强制保存会话, 即使任务已经完成, 默认:false # 较新的版本开启后会在任务完成后依然保留.aria2文件 #force-save=false # BT校验相关, 默认:true #bt-hash-check-seed=true # 继续之前的BT任务时, 无需再次校验, 默认:false bt-seed-unverified=true # 保存磁力链接元数据为种子文件(.torrent文件), 默认:false bt-save-metadata=true bt-tracker=udp://tracker.coppersurfer.tk:6969/announce,udp://tracker.opentrackr.org:1337/announce,udp://tracker.leechers-paradise.org:6969/announce,udp://p4p.arenabg.com:1337/announce,udp://9.rarbg.to:2710/announce,udp://9.rarbg.me:2710/announce,udp://tracker.internetwarriors.net:1337/announce,udp://exodus.desync.com:6969/announce,udp://tracker.cyberia.is:6969/announce,udp://retracker.lanta-net.ru:2710/announce,udp://tracker.tiny-vps.com:6969/announce,udp://open.stealth.si:80/announce,udp://tracker.torrent.eu.org:451/announce,udp://tracker3.itzmx.com:6961/announce,udp://tracker.moeking.me:6969/announce,udp://bt1.archive.org:6969/announce,udp://ipv4.tracker.harry.lu:80/announce,udp://bt2.archive.org:6969/announce,udp://zephir.monocul.us:6969/announce,udp://valakas.rollo.dnsabr.com:2710/announce,udp://tracker.zerobytes.xyz:1337/announce,udp://tracker.uw0.xyz:6969/announce,udp://tracker.lelux.fi:6969/announce,udp://tracker.kamigami.org:2710/announce,udp://tracker.ds.is:6969/announce,udp://retracker.akado-ural.ru:80/announce,udp://opentracker.i2p.rocks:6969/announce,udp://opentor.org:2710/announce,udp://explodie.org:6969/announce,udp://tracker-udp.gbitt.info:80/announce,udp://chihaya.de:6969/announce,udp://www.loushao.net:8080/announce,udp://u.wwwww.wtf:1/announce,udp://tracker.yoshi210.com:6969/announce,udp://tracker.teambelgium.net:6969/announce,udp://tracker.swateam.org.uk:2710/announce,udp://tracker.skyts.net:6969/announce,udp://tracker.jae.moe:6969/announce,udp://tracker.army:6969/announce,udp://tr2.ysagin.top:2710/announce,udp://t3.leech.ie:1337/announce,udp://t1.leech.ie:1337/announce,udp://retracker.sevstar.net:2710/announce,udp://retracker.netbynet.ru:2710/announce,udp://qg.lorzl.gq:2710/announce,udp://aaa.army:8866/announce,udp://tracker6.dler.org:2710/announce,udp://tracker4.itzmx.com:2710/announce,udp://tracker2.itzmx.com:6961/announce,udp://tracker.filemail.com:6969/announce,udp://tracker.dler.org:6969/announce,udp://tr.bangumi.moe:6969/announce,udp://bt2.54new.com:8080/announce配置系统服务vim /etc/systemd/system/aria2c.service[Unit] Description=Aria2c [Service] TimeoutStartSec=0 ExecStart=/usr/bin/aria2c --conf-path=/etc/aria2/aria2.conf ExecReload=/bin/kill -HUP $MAINPID KillMode=process Restart=on-failure RestartSec=42s [Install] WantedBy=multi-user.target启动与开机自启# 更新配置 systemctl daemon-reload # 启动服务 systemctl start aria2c # 设置开机启动 systemctl enable aria2c参考资料ubuntu安装aria2:https://www.jianshu.com/p/1178a669c308ubuntu Aria2 AriaNg安装:https://blog.csdn.net/macwinwin/article/details/106985341
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cloudreve私有云盘配置后台运行 cloudreve私有云盘配置后台运行在/usr/lib/systemd/system目录下创建一个服务cloudreve.servicesudo vim /etc/systemd/system/cloudreve.service输入如下内容：[Unit] Description=Cloudreve Documentation=https://docs.cloudreve.org [Service] TimeoutStartSec=0 ExecStart=/data/cloudreve/cloudreve ExecReload=/bin/kill -HUP $MAINPID KillMode=process Restart=on-failure RestartSec=5s [Install] WantedBy=multi-user.target更新重启服务# 更新配置 systemctl daemon-reload # 启动服务 systemctl start cloudreve # 设置开机启动 systemctl enable cloudreve 管理命令# 启动服务 systemctl start cloudreve # 停止服务 systemctl stop cloudreve # 重启服务 systemctl restart cloudreve # 查看状态 systemctl status cloudreve参考资料cloudreve私有云盘配置后台运行:https://blog.csdn.net/longzhoufeng/article/details/108958091
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python实现阿里云域名ipv4和ipv6 ddns 1.前言首先得有一个阿里云的域名：https://www.aliyun.com/minisite/goods?userCode=jdjc69nf然后你的IP必须是公网IP，不然解析了也没用。本文章讲怎样通过阿里云的SDK来添加修改域名解析，检查本机IP与解析的IP是否一致，不一致自动修改解析，达到动态解析的目的，主要用于家庭宽带这些动态IP的地方。2.安装阿里云SDK和其他第三方库pip install aliyun-python-sdk-core-v3 pip install aliyun-python-sdk-domain pip install aliyun-python-sdk-alidns pip install requests3.获取accessKeyId和accessSecret可以在阿里云控制台个人中心直接获取，但是一般建议使用RAM角色来进行权限控制，这样这个accessKey和accessSecret就只能操作域名，不能操作其他的资源，相对会比较安全。关于RAM快速入门：https://help.aliyun.com/document_detail/28637.html?source=5176.11533457&userCode=jdjc69nf4.源码下载-官方4.1 下载地址gitee：https://gitee.com/zeruns/aliddns_Pythongithub：https://github.com/zeruns/-Python-aliddns_ipv4-ipv6将aliddns.py文件下载下来。然后用notepad++或其他编辑器打开，按照注释提示修改并保存。然后运行一下看看有没有问题：打开cmd输入python 脚本目录4.2源码备份from aliyunsdkcore.client import AcsClient from aliyunsdkcore.acs_exception.exceptions import ClientException from aliyunsdkcore.acs_exception.exceptions import ServerException from aliyunsdkalidns.request.v20150109.DescribeSubDomainRecordsRequest import DescribeSubDomainRecordsRequest from aliyunsdkalidns.request.v20150109.DescribeDomainRecordsRequest import DescribeDomainRecordsRequest import requests from urllib.request import urlopen import json ipv4_flag = 1 # 是否开启ipv4 ddns解析,1为开启，0为关闭 ipv6_flag = 1 # 是否开启ipv6 ddns解析,1为开启，0为关闭 accessKeyId = "accessKeyId" # 将accessKeyId改成自己的accessKeyId accessSecret = "accessSecret" # 将accessSecret改成自己的accessSecret domain = "4v7p.top" # 你的主域名 name_ipv4 = "ipv4.test" # 要进行ipv4 ddns解析的子域名 name_ipv6 = "ipv6.test" # 要进行ipv6 ddns解析的子域名 client = AcsClient(accessKeyId, accessSecret, 'cn-hangzhou') def update(RecordId, RR, Type, Value): # 修改域名解析记录 from aliyunsdkalidns.request.v20150109.UpdateDomainRecordRequest import UpdateDomainRecordRequest request = UpdateDomainRecordRequest() request.set_accept_format('json') request.set_RecordId(RecordId) request.set_RR(RR) request.set_Type(Type) request.set_Value(Value) response = client.do_action_with_exception(request) def add(DomainName, RR, Type, Value): # 添加新的域名解析记录 from aliyunsdkalidns.request.v20150109.AddDomainRecordRequest import AddDomainRecordRequest request = AddDomainRecordRequest() request.set_accept_format('json') request.set_DomainName(DomainName) request.set_RR(RR) # https://blog.zeruns.tech request.set_Type(Type) request.set_Value(Value) response = client.do_action_with_exception(request) if ipv4_flag == 1: request = DescribeSubDomainRecordsRequest() request.set_accept_format('json') request.set_DomainName(domain) request.set_SubDomain(name_ipv4 + '.' + domain) response = client.do_action_with_exception(request) # 获取域名解析记录列表 domain_list = json.loads(response) # 将返回的JSON数据转化为Python能识别的 ip = urlopen('https://api-ipv4.ip.sb/ip').read() # 使用IP.SB的接口获取ipv4地址 ipv4 = str(ip, encoding='utf-8') print("获取到IPv4地址：%s" % ipv4) if domain_list['TotalCount'] == 0: add(domain, name_ipv4, "A", ipv4) print("新建域名解析成功") elif domain_list['TotalCount'] == 1: if domain_list['DomainRecords']['Record'][0]['Value'].strip() != ipv4.strip(): update(domain_list['DomainRecords']['Record'][0]['RecordId'], name_ipv4, "A", ipv4) print("修改域名解析成功") else: # https://blog.zeruns.tech print("IPv4地址没变") elif domain_list['TotalCount'] > 1: from aliyunsdkalidns.request.v20150109.DeleteSubDomainRecordsRequest import DeleteSubDomainRecordsRequest request = DeleteSubDomainRecordsRequest() request.set_accept_format('json') request.set_DomainName(domain) # https://blog.zeruns.tech request.set_RR(name_ipv4) response = client.do_action_with_exception(request) add(domain, name_ipv4, "A", ipv4) print("修改域名解析成功") print("本程序版权属于zeruns，博客：https://blog.zeruns.tech") if ipv6_flag == 1: request = DescribeSubDomainRecordsRequest() request.set_accept_format('json') request.set_DomainName(domain) request.set_SubDomain(name_ipv6 + '.' + domain) response = client.do_action_with_exception(request) # 获取域名解析记录列表 domain_list = json.loads(response) # 将返回的JSON数据转化为Python能识别的 ip = urlopen('https://api-ipv6.ip.sb/ip').read() # 使用IP.SB的接口获取ipv6地址 ipv6 = str(ip, encoding='utf-8') print("获取到IPv6地址：%s" % ipv6) if domain_list['TotalCount'] == 0: add(domain, name_ipv6, "AAAA", ipv6) print("新建域名解析成功") elif domain_list['TotalCount'] == 1: if domain_list['DomainRecords']['Record'][0]['Value'].strip() != ipv6.strip(): update(domain_list['DomainRecords']['Record'][0]['RecordId'], name_ipv6, "AAAA", ipv6) print("修改域名解析成功") else: # https://blog.zeruns.tech print("IPv6地址没变") elif domain_list['TotalCount'] > 1: from aliyunsdkalidns.request.v20150109.DeleteSubDomainRecordsRequest import DeleteSubDomainRecordsRequest request = DeleteSubDomainRecordsRequest() request.set_accept_format('json') request.set_DomainName(domain) request.set_RR(name_ipv6) # https://blog.zeruns.tech response = client.do_action_with_exception(request) add(domain, name_ipv6, "AAAA", ipv6) print("修改域名解析成功")6.设置定时任务6.1 Linux操作使用crontab -e写入定时任务即可。例如：6.2 windows操作右键点击电脑左下角，再点击计算机管理点击任务计划程序，再点击创建任务，输入要设置的任务名称。新建触发器，执行间隔可以自己设置，持续时间改成无限期。新建操作，这一步很重要，配置错误就会导致脚本文件执行不成功！！！最后确认就行。参考资料Python实现阿里云域名DDNS支持ipv4和ipv6：https://developer.aliyun.com/article/755182
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IOU计算 IOU计算&判断两个矩形相交以及求出相交的区域求解图示一$$ IOU=area/(area1+area2-area) $$求解图示二理论分析-判断两个矩形相交以及求出相交的区域问题：给定两个矩形A和B，矩形A的左上角坐标为（Xa1,Ya1），右下角坐标为（Xa2,Ya2），矩形B的左上角坐标为（Xb1,Yb1），右下角 坐标为（Xb2,Yb2）。1.设计一个算法，确定两个矩形是否相交（即有重叠区域）对于这个问题，一般的思路就是判断一个矩形的四个顶点是否在另一个矩形的区域内。这个思路最简单，但是效率不高，并且存在错误，错误在哪里，下面分析一 下。 如上图，把矩形的相交（区域重叠）分成三种（可能也有其他划分），对于第三种情况，如图中的（3），两个矩形相交，但并不存在一个矩形的顶点在另一个矩形 内部。所以那种思路存在一个错误，对于这种情况的相交则检查不出。仔细观察上图，想到另一种思路，那就是判断两个矩形的中心坐标的水平和垂直距离，只要这两个值满足某种条件就可以相交。矩形A的宽 Wa = Xa2-Xa1 高 Ha = Ya2-Ya1矩形B的宽 Wb = Xb2-Xb1 高 Hb = Yb2-Yb1矩形A的中心坐标 (Xa3,Ya3) = （ (Xa2+Xa1)/2 ，(Ya2+Ya1)/2 ）矩形B的中心坐标 (Xb3,Yb3) = （ (Xb2+Xb1)/2 ，(Yb2+Yb1)/2 ）所以只要同时满足下面两个式子，就可以说明两个矩形相交。1） | Xb3-Xa3 | <= Wa/2 + Wb/22） | Yb3-Ya3 | <= Ha/2 + Hb/2即：| Xb2+Xb1-Xa2-Xa1 | <= Xa2-Xa1 + Xb2-Xb1| Yb2+Yb1-Ya2-Ya1 | <=Y a2-Ya1 + Yb2-Yb12.如果两个矩形相交，设计一个算法，求出相交的区域矩形Xc1 = max(Xa1,Xb1)Yc1 = max(Ya1,Yb1)Xc2 = min(Xa2,Xb2)Yc2 = min(Ya2,Yb2)这样就求出了矩形的相交区域。另外，注意到在不假设矩形相交的前提下，定义（Xc1,Yc1）,（Xc2,Yc2）,且Xc1,Yc1,Xc2,Yc2的值由上面四个式子得出。这样， 可以依据Xc1,Yc1,Xc2,Yc2的值来判断矩形相交。Xc1,Yc1,Xc2,Yc2只要同时满足下面两个式子，就可以说明两个矩形相交。3) Xc1 <= Xc24) Yc1 <= Yc2即：max(Xa1,Xb1) <= min(Xa2,Xb2)max(Ya1,Yb1) <= min(Ya2,Yb2)代码实现代码""" IOU计算 + input + box1:[box1_x1,box1_y1,box1_x2,box1_y2] + box2:[box2_x1,box2_y1,box2_x2,box2_y2] + output + iou值 """ def cal_iou(box1,box2): # 判断是否能相交 if abs(box2[2]+box2[0]-box1[2]-box1[0])>box2[2]-box2[0]+box1[2]-box1[0]: return 0 if abs(box2[3]+box2[1]-box1[3]-box1[1])>box2[3]-box2[1]+box1[3]-box1[1]: return 0 # 求相交区域左上角的坐标和右下角的坐标 box_intersect_x1 = max(box1[0], box2[0]) box_intersect_y1 = max(box1[1], box2[1]) box_intersect_x2 = min(box1[2], box2[2]) box_intersect_y2 = min(box1[3], box2[3]) # 求二者相交的面积 area_intersect = (box_intersect_y2 - box_intersect_y1) * (box_intersect_x2 - box_intersect_x1) # 求box1,box2的面积 area_box1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) area_box2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) # 求二者相并的面积 area_union = area_box1 + area_box2 - area_intersect # 计算iou（交并比） iou = area_intersect / area_union return iou验证box1 = [0,0,500,500] box2 = [250,250,750,750] iou = cal_iou(box1,box2) print(iou)0.14285714285714285人为验证图示import matplotlib.pyplot as plt fig1 = plt.figure() ax1 = fig1.add_subplot(111, aspect='equal') ax1.add_patch(plt.Rectangle((0, 0),500,500,color='b',alpha=0.5)) ax1.add_patch(plt.Rectangle((250, 250),500,500,color='b',alpha=0.5)) ax1.add_patch(plt.Rectangle((250, 250),250,250,color='r',alpha=0.5)) plt.xlim(0, 750) plt.ylim(0, 750) plt.show()由图易知：area_box1= 250000area_box2= 250000area_intersect= 62500area_union= 437500因此：iou = 62500 / 437500 = 0.14285714285714285参考资料yolo 算法中的IOU算法程序与原理解读:https://blog.csdn.net/caokaifa/article/details/80724842IOU的计算:https://www.cnblogs.com/darkknightzh/p/9043395.html判断两个矩形相交以及求出相交的区域:https://www.cnblogs.com/zhoug2020/p/7451340.html
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深度学习常用数据集[转载] 深度学习常用数据集[转载]1、迁移学习（传统神经网络）1、猫狗数据集：链接：https://pan.baidu.com/s/1TqmdkJBY49ftg19tRK2Ngg 提取码: htxf2、目标检测1、VOC2007+2012训练集链接: https://pan.baidu.com/s/1u4YUyWJqs5bD38A6Hvs-og 提取码: xzde3、实例分割1、shape数据集（圆形、三角形、正方形）：链接: https://pan.baidu.com/s/14dBd1Lbjw0FCnwKryf9taQ 提取码: 94574、语义分割（旧版）1、斑马线数据集：链接：https://pan.baidu.com/s/1uzwqLaCXcWe06xEXk1ROWw 提取码：pp6w2、VOC数据集：链接：https://pan.baidu.com/s/1Urh9W7XPNMF8yR67SDjAAQ 提取码: cvy25、语义分割（新版）1、VOC拓展数据集及其验证集链接: https://pan.baidu.com/s/1BrR7AUM1XJvPWjKMIy2uEw 提取码: vszf6、人脸识别人脸识别数据集包含在对应权值的百度网盘里。1、retinaface链接: https://pan.baidu.com/s/1t7-BNsZzHj2isCekc_PVtw 提取码: 2qrs2、retinaface-pytorch链接: https://pan.baidu.com/s/1q2E6uWs0R5GU_PFs9_vglg 提取码: z7es参考资料1.神经网络学习小记录44——训练资源汇总贴：https://blog.csdn.net/weixin_44791964/article/details/105123842
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YOLOv3学习：（三）模型输出解码 YOLOv3学习：（三）模型输出解码YOLOv3 模型输出输出模型输出解码-理论(以13*13为例)解码目标模型输出shape:[batch_size, 255, 13, 13] 255 = 3(先验框数量)*(x_offset+y_offset+w_scale+h_scale+有无物体置信度+类别置信度)即原模型将图像分割为13*13的小块进行预测，每个小块负责根据先验框预测3个框，每个预测框以小格的左上角为基准点，以先验框的w和h为基准。$$ 预测框w=先验框w \times e^{w\_scale} $$$$ 预测框h=先验框h \times e^{h\_scale} $$模型输出解码的目标即为将输出结果的x_offset+y_offset+w_scale+h_scale部分进行校正，变成以整个图片的最左上角(0,0)点为基准点，并对每个预测框的w，h根据先验框进行对应校正。最终的到3*13*13个预测框。即解码输出shape:[batch_size, 3*13*13,85] 85=x_offset+y_offset+w_scale+h_scale+有无物体置信度+类别置信度模型输出解码-代码# YOLOv3 超参数 from easydict import EasyDict super_param = \ { "anchors": [[[116, 90], [156, 198], [373, 326]], [[30, 61], [62, 45], [59, 119]], [[10, 13], [16, 30], [33, 23]]], "num_classes": 80, "img_size":(416,416), } super_param = EasyDict(super_param) print(super_param.img_size) # YOLOv3模型输出结果解码器 """ 模型输出结果解释： 以[batch_size, 255, 13, 13]为例 255 = 3(先验框数量)*(x_offset+y_offset+w+h+有无物体置信度+类别置信度) 代表将原图划分为13*13 然后每个小框负责预测3个框 每个框的中心点为(框的左上角x+x_offset,框的左上角y+y_offset) 每个框的w和h为 torch.exp(w.data) * anchor_w 和torch.exp(h.data) * anchor_h 解码输出结果解释： 实例对应输出shape为[batch_size，3*13*13，85],即共预测了3*13*13个boxm 每个box的具体参数为(x+y+w+h+有无物体置信度+80个类别置信度)共85个 """ class DecodeBox(nn.Module): def __init__(self, anchors = super_param.anchors[0], num_classes = super_param.num_classes, img_size = super_param.img_size): super(DecodeBox, self).__init__() self.anchors = anchors self.num_anchors = len(anchors) self.num_classes = num_classes self.img_size = img_size def forward(self, input): # 获取YOLOv3单路输出的结果shape信息 batch_size,input_height,input_width = input.size(0),input.size(2),input.size(3) # 计算步长 stride_h,stride_w = self.img_size[1] / input_height,self.img_size[0] / input_width # 把把先验框归一到特征层上 eg:[116, 90], [156, 198], [373, 326] --》[116/32, 90/32], [156/32, 198/32], [373/32, 326/32] scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in self.anchors] # 对预测结果进行reshape # eg:[batch_size, 255, 13, 13] -->[batch_size,num_anchors,input_height,input_width,5 + num_classes](batch_size,3,13,13,85) # 维度中的85包含了4+1+80，分别代表x_offset、y_offset、h和w、置信度、分类结果。 prediction = input.view(batch_size, self.num_anchors, 5 + self.num_classes, input_height, input_width).permute(0, 1, 3, 4, 2).contiguous() # 先验框的中心位置的调整参数 x_offset,y_offset = torch.sigmoid(prediction[..., 0]),torch.sigmoid(prediction[..., 1]) # 先验框的宽高调整参数 w,h = prediction[..., 2],prediction[..., 3] # Width.Height # 获得置信度，是否有物体 conf = torch.sigmoid(prediction[..., 4]) # 种类置信度 pred_cls = torch.sigmoid(prediction[..., 5:]) # Cls pred. FloatTensor = torch.cuda.FloatTensor if x_offset.is_cuda else torch.FloatTensor LongTensor = torch.cuda.LongTensor if x_offset.is_cuda else torch.LongTensor # 生成网格，先验框中心，网格左上角 grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_width, 1).repeat( batch_size * self.num_anchors, 1, 1).view(x_offset.shape).type(FloatTensor) grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_height, 1).t().repeat( batch_size * self.num_anchors, 1, 1).view(y_offset.shape).type(FloatTensor) # 生成先验框的宽高 anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0])) anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1])) anchor_w = anchor_w.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(w.shape) anchor_h = anchor_h.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(h.shape) # 计算调整后的先验框中心与宽高 pred_boxes = FloatTensor(prediction[..., :4].shape) pred_boxes[..., 0] = x_offset.data + grid_x pred_boxes[..., 1] = y_offset.data + grid_y pred_boxes[..., 2] = torch.exp(w.data) * anchor_w pred_boxes[..., 3] = torch.exp(h.data) * anchor_h # 用于将输出调整为相对于416x416的大小 _scale = torch.Tensor([stride_w, stride_h] * 2).type(FloatTensor) output = torch.cat((pred_boxes.view(batch_size, -1, 4) * _scale, conf.view(batch_size, -1, 1), pred_cls.view(batch_size, -1, self.num_classes)), -1) return output.data测试fake_out1 = torch.zeros((1,255,13,13)) print(fake_out1.shape) decoder = DecodeBox() out1_decode = decoder(fake_out1) print(out1_decode.shape)torch.Size([1, 255, 13, 13]) torch.Size([1, 507, 85])参考资料Pytorch 搭建自己的YOLO3目标检测平台（Bubbliiiing 深度学习 教程）:https://www.bilibili.com/video/BV1Hp4y1y788?p=11&spm_id_from=pageDriver
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【YOLOv3论文翻译】：YOLOv3：增量式的改进 【YOLOv3论文翻译】：YOLOv3：增量式的改进论文原文:YOLOv3: An Incremental Improvement摘要我们对YOLO进行了一系列更新！它包含一堆小设计，可以使系统的性能得到更新。我们也训练了一个新的、比较大的神经网络。虽然比上一版更大一些，但是精度也提高了。不用担心，它的速度依然很快。YOLOv3在320×320输入图像上运行时只需22ms，并能达到28.2mAP，其精度和SSD相当，但速度要快上3倍。使用之前0.5 IOU mAP的检测指标，YOLOv3的效果是相当不错。YOLOv3使用Titan X GPU，其耗时51ms检测精度达到57.9 AP50，与RetinaNet相比，其精度只有57.5 AP50，但却耗时198ms，相同性能的条件下YOLOv3速度比RetinaNet快3.8倍。与之前一样，所有代码在网址：https://pjreddie.com/yolo/。1. 引言有时候，一年内你主要都在玩手机，你知道吗？今年我没有做很多研究。我在Twitter上花了很多时间。研究了一下GAN。去年我留下了一点点的精力[12] [1]；我设法对YOLO进行了一些改进。但是，实话实说，除了仅仅一些小的改变使得它变得更好之外，没有什么超级有趣的事情。我也稍微帮助了其他人的一些研究。其实，这就是今天我要讲的内容。我们有一篇论文快截稿了，并且我们还缺一篇关于YOLO更新内容的文章作为引用，但是我们没有引用来源。因此准备写一篇技术报告！技术报告的好处是他们不需要引言，你们都知道我为什么写这个。所以引言的结尾可以作为阅读本文剩余内容的一个指引。首先我们会告诉你YOLOv3的方案。其次我们会告诉你我们是如何实现的。我们也会告诉你我们尝试过但并不奏效的一些事情。最后我们将探讨这些的意义。2. 方案这节主要介绍YOLOv3的方案：我们主要从其他人的研究工作里获得了一些好思路、好想法。我们还训练了一个新的、比其他网络更好的分类网络。为了方便您理解，我们将带您从头到尾贯穿整个模型系统。![图1.这个图来自Focal Loss论文[9]。YOLOv3的运行速度明显快于其他具有可比性能的检测方法。检测时间基于M40或Titan X（这两个基本上是相同的GPU）。](/usr/uploads/auto_save_image/f1b7a2d2167837f377fafa85701fb668.png)2.1 边界框预测按照YOLO9000，我们的系统也使用维度聚类得到的anchor框来预测边界框[15]。网络为每个边界框预测的4个坐标：tx、ty、tw、th。假设格子距离图像的左上角偏移量为（cx，cy），先验边界框宽度和高度分别为：pw、ph，则预测结果对应为：训练时我们使用误差平方和损失。如果某个预测坐标的真值是$\hat{t^*}$，那么梯度就是真值（从真值框计算而得）和预测值之差：$\hat{t^*}-t^*$。真实值可以很容易地通过变换上述公式得到。YOLOv3使用逻辑回归预测每个边界框是目标的分数。如果真实标签框与某个边界框重叠的面积比与其他任何边界框都大，那么这个先验边界框得分为1。按照[17]的做法，如果先验边界框不是最好的，但是确实与目标的真实标签框重叠的面积大于阈值，我们就会忽略这个预测。我们使用阈值为0.5。与[17]不同，我们的系统只为每个真实目标分配一个边界框。如果先验边界框未分配到真实目标，则不会产生坐标或类别预测的损失，只会产生是否是目标的损失。![图2.维度先验和位置预测的边界框。我们使用聚类质心的偏移量预测框的宽度和高度。我们使用sigmoid函数预测相对于滤波器应用位置的框的中心坐标。这个图公然引用于自己的论文[15]。](/usr/uploads/auto_save_image/cf8bd0eecaa2aefdb8f1e86fbe6a4961.png)2.2 分类预测每个边界框都会使用多标签分类来预测框中可能包含的类。我们不用softmax，而是用单独的逻辑分类器，因为我们发现前者对于提升网络性能没什么作用。在训练过程中，我们用binary cross-entropy（二元交叉熵）损失来预测类别。当我们转向更复杂的领域，例如Open Images Dataset [7]，上面的这种改变将变得很有用。这个数据集中有许多重叠的标签（例如女性和人）。使用softmax会强加这样一个假设——即每个框恰好只有一个类别，但通常情况并非如此。多标签的方式可以更好地模拟数据。2.3 跨尺度预测YOLOv3预测3种不同尺度的框。我们的系统使用类似特征金字塔网络的相似概念，并从这些尺度中提取特征[8]。在我们的基础特征提取器上添加了几个卷积层。其中最后一个卷积层预测了一个编码边界框、是否是目标和类别预测结果的三维张量。在我们的COCO实验[8]中，我们为每个尺度预测3个框，所以对于每个边界框的4个偏移量、1个目标预测和80个类别预测，最终的张量大小为N×N×[3×(4+1+80)]。接下来，我们从前面的2个层中取得特征图，并将其上采样2倍。我们还从网络中的较前的层中获取特征图，并将其与我们的上采样特征图进行拼接。这种方法使我们能够从上采样的特征图中获得更有意义的语义信息，同时可以从更前的层中获取更细粒度的信息。然后，我们添加几个卷积层来处理这个特征映射组合，并最终预测出一个相似的、大小是原先两倍的张量。我们再次使用相同的设计来预测最终尺寸的边界框。因此，第三个尺寸的预测将既能从所有先前的计算，又能从网络前面的层中的细粒度的特征中获益。我们仍然使用k-means聚类来确定我们的先验边界框。我们只是选择了9个类和3个尺度，然后在所有尺度上将聚类均匀地分开。在COCO数据集上，9个聚类分别为(10×13)、(16×30)、(33×23)、(30×61)、(62×45)、(59×119)、(116 × 90)、(156 × 198)、(373 × 326)。2.4 特征提取器我们使用一个新的网络来进行特征提取。我们的新网络融合了YOLOv2、Darknet-19和新发明的残差网络的思想。我们的网络使用连续的3×3和1×1卷积层，而且现在多了一些快捷连接（shortcut connetction），而且规模更大。它有53个卷积层，所以我们称之为... Darknet-53！这个新网络比Darknet-19功能强大很多，并且仍然比ResNet-101或ResNet-152更高效。以下是一些ImageNet上的结果：每个网络都使用相同的设置进行训练，并在256×256的图像上进行单精度测试。运行时间是在Titan X上用256×256图像进行测量的。因此，Darknet-53可与最先进的分类器相媲美，但浮点运算更少，速度更快。Darknet-53比ResNet-101更好，且速度快1.5倍。Darknet-53与ResNet-152相比性能差不多，但速度快比其2倍。Darknet-53也实现了最高的每秒浮点运算测量。这意味着网络结构可以更好地利用GPU，使它的评测更加高效、更快。这主要是因为ResNets的层数太多，效率不高。2.5 训练我们仍然在完整的图像上进行训练，没有使用难负样本挖掘（hard negative mining）或其他类似的方法。我们使用多尺度训练，使用大量的数据增强、批量标准化等标准的操作。我们使用Darknet神经网络框架进行训练和测试[12]。3 我们是如何做的YOLOv3表现非常好！请看表3。就COCO的平均AP指标而言，它与SSD类的模型相当，但速度提高了3倍。尽管如此，它仍然在这个指标上比像RetinaNet这样的其他模型差些。![表3.我很认真地从[9]中“窃取”了所有这些表格，他们花了很长时间才从头开始制作。好的，YOLOv3没问题。请记住，RetinaNet处理图像的时间要长3.8倍。YOLOv3比SSD变体要好得多，可与AP50指标上的最新模型相媲美。](/usr/uploads/auto_save_image/b315b290b4c82ed2f24a0538afbbfbd4.png)然而，当我们使用“旧的”检测指标——在IOU=0.5的mAP（或图表中的AP50）时，YOLOv3非常强大。其性能几乎与RetinaNet相当，并且远强于SSD。这表明YOLOv3是一个非常强大的检测器，擅长为目标生成恰当的框。然而，随着IOU阈值增加，性能显著下降，这表明YOLOv3预测的边界框与目标不能完美对齐。之前的YOLO不擅长检测小物体。但是，现在我们看到了这种趋势的逆转。随着新的多尺度预测，我们看到YOLOv3具有相对较高的APS性能。但是，它在中等和更大尺寸的物体上的表现相对较差。需要更多的研究来深入了解这一点。当我们在AP50指标上绘制准确度和速度关系图时（见图3），我们看到YOLOv3与其他检测系统相比具有显着的优势。也就是说，速度更快、性能更好。![图3. 再次改编自[9]，这次显示的是在0.5 IOU指标上速度/准确度的折衷。你可以说YOLOv3是好的，因为它非常高并且在左边很远。 你能引用你自己的论文吗？猜猜谁会去尝试，这个人→[16]。哦，我忘了，我们还修复了YOLOv2中的数据加载bug，该bug的修复提升了2 mAP。将YOLOv3结果潜入这幅图中而没有改变原始布局。](/usr/uploads/auto_save_image/d381f8d42ff1a78d2af931002d8d9127.png)4 失败的尝试我们在研究YOLOv3时尝试了很多东西，但很多都不起作用。下面是我们要记住的血的教训。Anchor框的x、y偏移预测。我们尝试使用常规的Anchor框预测机制，比如利用线性激活将坐标x、y的偏移程度预测为边界框宽度或高度的倍数。但我们发现这种方法降低了模型的稳定性，并且效果不佳。用线性激活代替逻辑激活函数进行x、y预测。我们尝试使用线性激活代替逻辑激活来直接预测x、y偏移。这个改变导致MAP下降了几个点。focal loss。我们尝试使用focal loss。它使得mAP下降2个点。YOLOv3可能已经对focal loss试图解决的问题具有鲁棒性，因为它具有单独的目标预测和条件类别预测。因此，对于大多数样本来说，类别预测没有损失？或者有一些？我们并不完全确定。双IOU阈值和真值分配。Faster R-CNN在训练期间使用两个IOU阈值。如果一个预测与真实标签框重叠超过0.7，它就是一个正样本，若重叠为[0.3，0.7]之间，那么它会被忽略，若它与所有的真实标签框的IOU小于0.3，那么一个负样本。我们尝试了类似的策略，但无法取得好的结果。我们非常喜欢目前的更新，它似乎至少在局部达到了最佳。有些方法可能最终会产生好的结果，也许他们只是需要一些调整来稳定训练。5 这一切意味着什么YOLOv3是一个很好的检测器。速度很快、很准确。它在COCO平均AP介于0.5和0.95 IOU之间的指标的上并不理想。但是，对于旧的0.5 IOU检测指标上效果非常好。为什么我们要改变指标？COCO的原论文只是有这样一句含糊不清的句子：“一旦评估服务器完成，就会生成全面评测指标”。Russakovsky等人的报告说，人们很难区分0.3和0.5的IOU。“训练人类用视觉检查0.3 IOU的边界框，并且与0.5 IOU的框区别开来是非常困难的。“[16]如果人类很难说出差异，那么它也没有多重要吧？但是也许更好的问题是：“现在我们有了这些检测器，我们要做什么？”很多做关于这方面的研究的人都受聘于Google和Facebook。我想至少我们知道这项技术在好人的手中，绝对不会被用来收集您的个人信息并将其出售给......等等，您是说这正是它的用途？oh。其他花大钱资助视觉研究的人还有军方，他们从来没有做过任何可怕的事情，例如用新技术杀死很多人，等等.....（脚注：作者由the Office of Naval Research and Google资助支持。）我强烈地希望，大多数使用计算机视觉的人都用它来做一些快乐且有益的事情，比如计算一个国家公园里斑马的数量[11]，或者追踪在附近徘徊的猫[17]。但是计算机视觉已经有很多可疑的用途，作为研究人员，我们有责任考虑我们的工作可能造成的损害，并思考如何减轻它的影响。我们欠这个世界太多。最后，不要再@我了。（因为哥已经退出Twitter这个是非之地了）。参考文献[1] Analogy. Wikipedia, Mar 2018. 1[2] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303–338, 2010. 6[3] C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg. Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659, 2017. 3[4] D. Gordon, A. Kembhavi, M. Rastegari, J. Redmon, D. Fox, and A. Farhadi. Iqa: Visual question answering in interactive environments. arXiv preprint arXiv:1712.03316, 2017. 1[5] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 3[6] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z.Wojna, Y. Song, S. Guadarrama, et al. Speed/accuracy trade-offs for modern convolutional object detectors. 3[7] I. Krasin, T. Duerig, N. Alldrin, V. Ferrari, S. Abu-El-Haija, A. Kuznetsova, H. Rom, J. Uijlings, S. Popov, A. Veit, S. Belongie, V. Gomes, A. Gupta, C. Sun, G. Chechik, D. Cai, Z. Feng, D. Narayanan, and K. Murphy. Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available fromhttps://github.com/openimages, 2017. 2[8] T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2117–2125, 2017. 2, 3[9] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Doll´ar. Focal loss for dense object detection. arXiv preprint arXiv:1708.02002, 2017. 1, 3, 4[10] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740–755. Springer, 2014. 2[11] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.- Y. Fu, and A. C. Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pages 21–37. Springer, 2016. 3[12] I. Newton. Philosophiae naturalis principia mathematica. William Dawson & Sons Ltd., London, 1687. 1[13] J. Parham, J. Crall, C. Stewart, T. Berger-Wolf, and D. Rubenstein. Animal population censusing at scale with citizen science and photographic identification. 2017. 4[14] J. Redmon. Darknet: Open source neural networks in c. http://pjreddie.com/darknet/, 2013–2016. 3[15] J. Redmon and A. Farhadi. Yolo9000: Better, faster, stronger. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, pages 6517–6525. IEEE, 2017. 1, 2, 3[16] J. Redmon and A. Farhadi. Yolov3: An incremental improvement. arXiv, 2018. 4[17] S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497, 2015. 2[18] O. Russakovsky, L.-J. Li, and L. Fei-Fei. Best of both worlds: human-machine collaboration for object annotation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2121–2131, 2015. 4[19] M. Scott. Smart camera gimbal bot scanlime:027, Dec 2017. 4[20] A. Shrivastava, R. Sukthankar, J. Malik, and A. Gupta. Beyond skip connections: Top-down modulation for object detection. arXiv preprint arXiv:1612.06851, 2016. 3[21] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. 2017. 3参考资料目标检测经典论文——YOLOv3论文翻译（纯中文版）：YOLOv3：增量式的改进（YOLOv3: An Incremental Improvement）：https://blog.csdn.net/Jwenxue/article/details/107749323?ops_request_misc=%25257B%252522request%25255Fid%252522%25253A%252522161268258716780274122037%252522%25252C%252522scm%252522%25253A%25252220140713.130102334.pc%25255Fblog.%252522%25257D&request_id=161268258716780274122037&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v1~rank_blog_v1-12-107749323.pc_v1_rank_blog_v1&utm_term=YOLO
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linux – /usr/lib/tracker/tracker-store占用大量CPU资源解决方案 linux – /usr/lib/tracker/tracker-store占用大量CPU资源解决方案问题描述电脑变得有点卡顿，htop查看发现linux – /usr/lib/tracker/tracker-store占用大量CPU资源 PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 7039 nath 20 0 96136 24460 11480 R 100,0 1,3 0:01.76 tracker-store杀死后会自动重启。冒险一搏解决方案# tracker reset --hard CAUTION: This process may irreversibly delete data. Although most content indexed by Tracker can be safely reindexed, it can?t be assured that this is the case for all data. Be aware that you may be incurring in a data loss situation, proceed at your own risk. Are you sure you want to proceed? [y|N]: y参考资料linux – /usr/lib / tracker / tracker-store导致Deb...:http://www.cocoachina.com/cms/wap.php?action=article&id=50994
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FFmpeg 简单使用 FFmpeg 简单使用一、相关概念1.1 容器视频文件本身其实是一个容器（container），里面包括了视频和音频，也可能有字幕等其他内容。常见的容器格式有以下几种。一般来说，视频文件的后缀名反映了它的容器格式。MP4MKVWebMAVI下面的命令查看 FFmpeg 支持的容器。ffmpeg -formats1.2 编码格式视频和音频都需要经过编码，才能保存成文件。不同的编码格式（CODEC），有不同的压缩率，会导致文件大小和清晰度的差异。常用的视频编码格式如下。H.262H.264H.265上面的编码格式都是有版权的，但是可以免费使用。此外，还有几种无版权的视频编码格式。VP8VP9AV1常用的音频编码格式如下。MP3AAC1.3 编码器编码器（encoders）是实现某种编码格式的库文件。只有安装了某种格式的编码器，才能实现该格式视频/音频的编码和解码。以下是一些 FFmpeg 内置的视频编码器:libx264：最流行的开源 H.264 编码器NVENC：基于 NVIDIA GPU 的 H.264 编码器libx265：开源的 HEVC 编码器libvpx：谷歌的 VP8 和 VP9 编码器libaom：AV1 编码器音频编码器如下:libfdk-aacaac下面的命令可以查看 FFmpeg 已安装的编码器:ffmpeg -encoders二、FFmpeg 的使用格式FFmpeg 的命令行参数非常多，可以分成五个部分。ffmpeg {1} {2} -i {3} {4} {5}上面命令中，五个部分的参数依次如下。全局参数输入文件参数输入文件输出文件参数输出文件参数太多的时候，为了便于查看，ffmpeg 命令可以写成多行。ffmpeg \ [全局参数] \ [输入文件参数] \ -i [输入文件] \ [输出文件参数] \ [输出文件]下面是一个例子。ffmpeg \ -y \ # 全局参数 -c:a libfdk_aac -c:v libx264 \ # 输入文件参数 -i input.mp4 \ # 输入文件 -c:v libvpx-vp9 -c:a libvorbis \ # 输出文件参数 output.webm # 输出文件上面的命令将 mp4 文件转成 webm 文件，这两个都是容器格式。输入的 mp4 文件的音频编码格式是 aac，视频编码格式是 H.264；输出的 webm 文件的视频编码格式是 VP9，音频格式是 Vorbis。如果不指明编码格式，FFmpeg 会自己判断输入文件的编码。因此，上面的命令可以简单写成下面的样子。ffmpeg -i input.avi output.mp4三、常用命令行参数FFmpeg 常用的命令行参数如下。-c：指定编码器-c copy：直接复制，不经过重新编码（这样比较快）-c:v：指定视频编码器-c:a：指定音频编码器-i：指定输入文件-an：去除音频流-vn： 去除视频流-preset：指定输出的视频质量，会影响文件的生成速度，有以下几个可用的值 ultrafast, superfast, veryfast, faster, fast, medium, slow, slower, veryslow。-y：不经过确认，输出时直接覆盖同名文件。四、常见用法4.1 查看文件信息查看视频文件的元信息，比如编码格式和比特率，可以只使用-i参数。ffmpeg -i input.mp4上面命令会输出很多冗余信息，加上-hide_banner参数，可以只显示元信息。ffmpeg -i input.mp4 -hide_banner4.2 转换编码格式转换编码格式（transcoding）指的是， 将视频文件从一种编码转成另一种编码。比如转成 H.264 编码，一般使用编码器libx264，所以只需指定输出文件的视频编码器即可。ffmpeg -i [input.file] -c:v libx264 output.mp4下面是转成 H.265 编码的写法。ffmpeg -i [input.file] -c:v libx265 output.mp44.2 转换编码格式转换编码格式（transcoding）指的是， 将视频文件从一种编码转成另一种编码。比如转成 H.264 编码，一般使用编码器libx264，所以只需指定输出文件的视频编码器即可。ffmpeg -i [input.file] -c:v libx264 output.mp4下面是转成 H.265 编码的写法。ffmpeg -i [input.file] -c:v libx265 output.mp44.3 转换容器格式转换容器格式（transmuxing）指的是，将视频文件从一种容器转到另一种容器。下面是 mp4 转 webm 的写法。ffmpeg -i input.mp4 -c copy output.webm上面例子中，只是转一下容器，内部的编码格式不变，所以使用-c copy指定直接拷贝，不经过转码，这样比较快。4.4 调整码率调整码率（transrating）指的是，改变编码的比特率，一般用来将视频文件的体积变小。下面的例子指定码率最小为964K，最大为3856K，缓冲区大小为 2000K。ffmpeg \ -i input.mp4 \ -minrate 964K -maxrate 3856K -bufsize 2000K \ output.mp44.5 改变分辨率（transsizing）下面是改变视频分辨率（transsizing）的例子，从 1080p 转为 480p 。ffmpeg \ -i input.mp4 \ -vf scale=480:-1 \ output.mp44.6 提取音频有时，需要从视频里面提取音频（demuxing），可以像下面这样写。ffmpeg \ -i input.mp4 \ -vn -c:a copy \ output.aac上面例子中，-vn表示去掉视频，-c:a copy表示不改变音频编码，直接拷贝。4.7 添加音轨添加音轨（muxing）指的是，将外部音频加入视频，比如添加背景音乐或旁白。ffmpeg \ -i input.aac -i input.mp4 \ output.mp4上面例子中，有音频和视频两个输入文件，FFmpeg 会将它们合成为一个文件。4.8 截图下面的例子是从指定时间开始，连续对1秒钟的视频进行截图。ffmpeg \ -y \ -i input.mp4 \ -ss 00:01:24 -t 00:00:01 \ output_%3d.jpg如果只需要截一张图，可以指定只截取一帧。ffmpeg \ -ss 01:23:45 \ -i input \ -vframes 1 -q:v 2 \ output.jpg上面例子中，-vframes 1指定只截取一帧，-q:v 2表示输出的图片质量，一般是1到5之间（1 为质量最高）。4.9 裁剪裁剪（cutting）指的是，截取原始视频里面的一个片段，输出为一个新视频。可以指定开始时间（start）和持续时间（duration），也可以指定结束时间（end）。ffmpeg -ss [start] -i [input] -t [duration] -c copy [output] ffmpeg -ss [start] -i [input] -to [end] -c copy [output]下面是实际的例子。ffmpeg -ss 00:01:50 -i [input] -t 10.5 -c copy [output] ffmpeg -ss 2.5 -i [input] -to 10 -c copy [output]上面例子中，-c copy表示不改变音频和视频的编码格式，直接拷贝，这样会快很多。4.10 使用ffmpeg合并多个视频文件方法1、直接写文件名，使用“|”来分割： ffmpeg -i "concat:cd1.mp4|cd2.mp4" -c copy out.mp4方法2、先编辑一个txt的文本文件，其中罗列了需要合并的子文件路径和名称：ffmpeg -f concat -safe 0 -i filelist.txt -c copy output.mp4filelist.txt的内容：file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.1.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.2.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.3.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.4.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.5.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.6.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.7.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.8.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.9.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.10.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.11.ts' file 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.12.ts'注意：为了防止出现“Unsafe file name 'D:\delphisr\腾讯下载地址解析子串\k0028qzpkdl.321002.1.ts'filelist.txt: Operation not permitted” 必须在命令中加入“-safe 0”参考资料FFmpeg 视频处理入门教程：http://www.ruanyifeng.com/blog/2020/01/ffmpeg.html使用ffmpeg合并多个视频文件:https://blog.csdn.net/winniezhang/article/details/89260841
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YOLOv3学习：（二）网络结构推导与实现 YOLOv3学习：（二）网络结构推导与实现网络结构图简版：网络结构图简版+特征图的大小变换：网络结构-详细版网络结构模块化网络结构图展开(超详细版)网络结构+示例-3D版（利用多尺度特征进行对象检测）9种尺度的先验框随着输出的特征图的数量和尺度的变化，先验框的尺寸也需要相应的调整。YOLO2已经开始采用K-means聚类得到先验框的尺寸，YOLO3延续了这种方法，为每种下采样尺度设定3种先验框，总共聚类出9种尺寸的先验框。在COCO数据集这9个先验框是：(10x13)，(16x30)，(33x23)，(30x61)，(62x45)，(59x119)，(116x90)，(156x198)，(373x326)。分配上，在最小的1313特征图上（有最大的感受野）应用较大的先验框(116x90)，(156x198)，(373x326)，适合检测较大的对象。中等的2626特征图上（中等感受野）应用中等的先验框(30x61)，(62x45)，(59x119)，适合检测中等大小的对象。较大的52*52特征图上（较小的感受野）应用较小的先验框(10x13)，(16x30)，(33x23)，适合检测较小的对象。感受一下9种先验框的尺寸，下图中蓝色框为聚类得到的先验框。黄色框式ground truth，红框是对象中心点所在的网格。输入到输出的映射(包含输出参数的解释)不考虑神经网络结构细节的话，总的来说，对于一个输入图像，YOLO3将其映射到3个尺度的输出张量，代表图像各个位置存在各种对象的概率。我们看一下YOLO3共进行了多少个预测。对于一个416416的输入图像，在每个尺度的特征图的每个网格设置3个先验框，总共有 13133 + 26263 + 5252*3 = 10647 个预测。每一个预测是一个(4+1+80)=85维向量，这个85维向量包含边框坐标（4个数值），边框置信度（1个数值），对象类别的概率（对于COCO数据集，有80种对象）。对比一下，YOLO2采用13135 = 845个预测，YOLO3的尝试预测边框数量增加了10多倍，而且是在不同分辨率上进行，所以mAP以及对小物体的检测效果有一定的提升。代码实现代码import torch import torch.nn as nn # Darknet53 中的基本块--卷积块，由Conv+BN+LeakyReLU共同组成 class ConvBNReLU(nn.Module): def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super(ConvBNReLU,self).__init__() self.conv = nn.Conv2d(in_channels,out_channels,kernel_size,stride,padding) self.BN = nn.BatchNorm2d(out_channels) self.leaky_relu = nn.ReLU6(inplace=True) def forward(self,x): x = self.conv(x) x = self.BN(x) x = self.leaky_relu(x) return x # Darknet53 中的基本块--下采样块,用卷积(stride=2)实现 class DownSample(nn.Module): def __init__(self,in_channels,out_channels): super(DownSample,self).__init__() self.down_samp = nn.Conv2d(in_channels,out_channels,3,2,1) def forward(self,x): x = self.down_samp(x) return x # Darknet53 中的基本块--ResBlock class ResBlock(nn.Module): def __init__(self, nchannels): super(ResBlock, self).__init__() mid_channels = nchannels // 2 self.conv1x1 = ConvBNReLU(nchannels, mid_channels,1,1,0) self.conv3x3 = ConvBNReLU(mid_channels, nchannels,3,1,1) def forward(self, x): out = self.conv3x3(self.conv1x1(x)) return out + x # YOLOv3 骨干网络 -DarkNet53 class DarkNet53_YOLOv3(nn.Module): def __init__(self): super(DarkNet53_YOLOv3, self).__init__() self.conv_bn_relu = ConvBNReLU(3,32,3,1,1) self.down_samp_0 = DownSample(32,64) self.res_block_1 = ResBlock(64) self.down_samp_1 = DownSample(64,128) self.res_block_2 = ResBlock(128) self.down_samp_2 = DownSample(128,256) self.res_block_3 = ResBlock(256) self.down_samp_3 = DownSample(256,512) self.res_block_4 = ResBlock(512) self.down_samp_4 = DownSample(512,1024) self.res_block_5 = ResBlock(1024) def forward(self, x): out1 = self.conv_bn_relu(x) out1 = self.down_samp_0(out1) out1 = self.res_block_1(out1) out1 = self.down_samp_1(out1) out1 = self.res_block_2(out1) out1 = self.res_block_2(out1) out1 = self.down_samp_2(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out1 = self.res_block_3(out1) out2 = self.down_samp_3(out1) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out2 = self.res_block_4(out2) out3 = self.down_samp_4(out2) out3 = self.res_block_5(out3) out3 = self.res_block_5(out3) out3 = self.res_block_5(out3) out3 = self.res_block_5(out3) out3 = self.res_block_5(out3) return out1,out2,out3 # YOLOv3 13*13 输出分支的darknet53后的几层 class Out1LastLayers(nn.Module): #input_shape = (1024, 13, 13) out_shape = (255,13,13) out_branck_shape = (512,13,13) def __init__(self): super(Out1LastLayers, self).__init__() self.conv1x1 = ConvBNReLU(1024,512,1,1,0) self.conv3x3 = ConvBNReLU(512, 1024,3,1,1) self.conv1x1_last = ConvBNReLU(1024,255,1,1,0) def forward(self,x): out = self.conv1x1(x) out = self.conv3x3(out) out = self.conv1x1(out) out = self.conv3x3(out) out = self.conv1x1(out) out_branch = out out = self.conv3x3(out) out = self.conv1x1_last(out) return out,out_branch # YOLOv3 26*26 输出分支的darknet53后的几层 class Out2LastLayers(nn.Module): #input_shape = (512, 26, 26) out_shape = (255,26,26) out_branck_shape = (256,26,26) def __init__(self): super(Out2LastLayers, self).__init__() self.conv1x1 = ConvBNReLU(512,256,1,1,0) self.conv3x3 = ConvBNReLU(256,512,3,1,1) self.up_sample = nn.Upsample(scale_factor=2, mode='nearest') self.conv1x1_after_concat = ConvBNReLU(768,256,1,1,0) self.conv1x1_last = ConvBNReLU(512,255,1,1,0) def forward(self,x,x_branch): out = self.conv1x1(x_branch) out = self.up_sample(out) out = torch.cat([x,out],1) out = self.conv1x1_after_concat(out) out = self.conv3x3(out) out = self.conv1x1(out) out = self.conv3x3(out) out = self.conv1x1(out) out_branch = out out = self.conv3x3(out) out = self.conv1x1_last(out) return out,out_branch # YOLOv3 52*52 输出分支的darknet53后的几层 class Out3LastLayers(nn.Module): #input_shape = (256, 52, 52) out_shape = (255,52,52) def __init__(self): super(Out3LastLayers, self).__init__() self.conv1x1 = ConvBNReLU(256,128,1,1,0) self.conv3x3 = ConvBNReLU(128,256,3,1,1) self.up_sample = nn.Upsample(scale_factor=2, mode='nearest') self.conv1x1_after_concat = ConvBNReLU(384,128,1,1,0) self.conv1x1_last = ConvBNReLU(256,255,1,1,0) def forward(self,x,x_branch): out = self.conv1x1(x_branch) out = self.up_sample(out) out = torch.cat([x,out],1) out = self.conv1x1_after_concat(out) out = self.conv3x3(out) out = self.conv1x1(out) out = self.conv3x3(out) out = self.conv1x1(out) out = self.conv3x3(out) out = self.conv1x1_last(out) return out # YOLOv3模型 class YOLOv3(nn.Module): def __init__(self): super(YOLOv3, self).__init__() self.darknet53 = DarkNet53_YOLOv3() self.out1_last_layers = Out1LastLayers() self.out2_last_layers = Out2LastLayers() self.out3_last_layers = Out3LastLayers() def forward(self, x): out3,out2,out1 = self.darknet53(x) # out1.shape,out2.shape,out3.shape = (256, 52, 52),(512, 26, 26),(1024, 13, 13) out1,out1_branch = self.out1_last_layers(out1) out2,out2_branch = self.out2_last_layers(out2,out1_branch) out3 = self.out3_last_layers(out3,out2_branch) return out1,out2,out3输入输出测试fake_input = torch.zeros((1,3,416,416)) print(fake_input.shape) model = YOLOv3() out1,out2,out3= model(fake_input) print(out1.shape,out2.shape,out3.shape)torch.Size([1, 3, 416, 416]) torch.Size([1, 255, 13, 13]) torch.Size([1, 255, 26, 26]) torch.Size([1, 255, 52, 52])参考资料YOLOv3网络结构和解析：https://blog.csdn.net/dz4543/article/details/90049377Darknet53网络各层参数详解：https://blog.csdn.net/qq_40210586/article/details/106144197目标检测0-02：YOLO V3-网络结构输入输出解析：https://blog.csdn.net/weixin_43013761/article/details/98349080YOLOv3 深入理解：https://www.jianshu.com/p/d13ae1055302
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YOLOv3学习：（一）Darknet-53结构推导与实现 YOLOv3学习：（一）Darknet-53结构推导与实现原生Darknet-53网络结构代码实现-1（更易读）模型代码import torch import torch.nn as nn # Darknet53 中的基本块--卷积块，由Conv+BN+LeakyReLU共同组成 class ConvBNReLU(nn.Module): def __init__(self,in_channels,out_channels,kernel_size,stride,padding): super(ConvBNReLU,self).__init__() self.conv = nn.Conv2d(in_channels,out_channels,kernel_size,stride,padding) self.BN = nn.BatchNorm2d(out_channels) self.leaky_relu = nn.ReLU6(inplace=True) def forward(self,x): x = self.conv(x) x = self.BN(x) x = self.leaky_relu(x) return x # Darknet53 中的基本块--下采样块,用卷积(stride=2)实现 class DownSample(nn.Module): def __init__(self,in_channels,out_channels): super(DownSample,self).__init__() self.down_samp = nn.Conv2d(in_channels,out_channels,3,2,1) def forward(self,x): x = self.down_samp(x) return x # Darknet53 中的基本块--ResBlock class ResBlock(nn.Module): def __init__(self, nchannels): super(ResBlock, self).__init__() mid_channels = nchannels // 2 self.conv1x1 = ConvBNReLU(nchannels, mid_channels,1,1,0) self.conv3x3 = ConvBNReLU(mid_channels, nchannels,3,1,1) def forward(self, x): out = self.conv3x3(self.conv1x1(x)) return out + x num_classes=1000 darknet53= nn.Sequential() darknet53.add_module('conv_bn_relu',ConvBNReLU(3,32,3,1,1)) darknet53.add_module('down_samp_0',DownSample(32,64)) darknet53.add_module('res_block_1_1',ResBlock(64)) darknet53.add_module('down_samp_1',DownSample(64,128)) darknet53.add_module('res_block_2_1',ResBlock(128)) darknet53.add_module('res_block_2_2',ResBlock(128)) darknet53.add_module('down_samp_2',DownSample(128,256)) darknet53.add_module('res_block_3_1',ResBlock(256)) darknet53.add_module('res_block_3_2',ResBlock(256)) darknet53.add_module('res_block_3_3',ResBlock(256)) darknet53.add_module('res_block_3_4',ResBlock(256)) darknet53.add_module('res_block_3_5',ResBlock(256)) darknet53.add_module('res_block_3_6',ResBlock(256)) darknet53.add_module('res_block_3_7',ResBlock(256)) darknet53.add_module('res_block_3_8',ResBlock(256)) darknet53.add_module('down_samp_3',DownSample(256,512)) darknet53.add_module('res_block_4_1',ResBlock(512)) darknet53.add_module('res_block_4_2',ResBlock(512)) darknet53.add_module('res_block_4_3',ResBlock(512)) darknet53.add_module('res_block_4_4',ResBlock(512)) darknet53.add_module('res_block_4_5',ResBlock(512)) darknet53.add_module('res_block_4_6',ResBlock(512)) darknet53.add_module('res_block_4_7',ResBlock(512)) darknet53.add_module('res_block_4_8',ResBlock(512)) darknet53.add_module('down_samp_4',DownSample(512,1024)) darknet53.add_module('res_block_5_1',ResBlock(1024)) darknet53.add_module('res_block_5_2',ResBlock(1024)) darknet53.add_module('res_block_5_3',ResBlock(1024)) darknet53.add_module('res_block_5_4',ResBlock(1024)) darknet53.add_module('avg_pool',nn.AvgPool2d(kernel_size=8,stride=1)) darknet53.add_module('flatten',nn.Flatten()) darknet53.add_module('linear',nn.Linear(in_features=1024,out_features=num_classes)) darknet53.add_module('softmax',nn.Softmax(dim=1)) print(darknet53)输入输出验证fake_imput = torch.zeros((1,3,256,256)) print(fake_imput.shape) output = darknet53(fake_imput) print(output.shape)torch.Size([1, 3, 256, 256]) torch.Size([1, 1000])代码实现-2（代码更少）import torch import torch.nn as nn def Conv3x3BNReLU(in_channels,out_channels,stride=1): return nn.Sequential( nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=3,stride=stride,padding=1), nn.BatchNorm2d(out_channels), nn.ReLU6(inplace=True) ) def Conv1x1BNReLU(in_channels,out_channels): return nn.Sequential( nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=1,stride=1,padding=0), nn.BatchNorm2d(out_channels), nn.ReLU6(inplace=True) ) class Residual(nn.Module): def __init__(self, nchannels): super(Residual, self).__init__() mid_channels = nchannels // 2 self.conv1x1 = Conv1x1BNReLU(in_channels=nchannels, out_channels=mid_channels) self.conv3x3 = Conv3x3BNReLU(in_channels=mid_channels, out_channels=nchannels) def forward(self, x): out = self.conv3x3(self.conv1x1(x)) return out + x class Darknet53(nn.Module): def __init__(self, num_classes=1000): super(Darknet53, self).__init__() self.first_conv = Conv3x3BNReLU(in_channels=3, out_channels=32) self.block1 = self._make_layers(in_channels=32,out_channels=64, block_num=1) self.block2 = self._make_layers(in_channels=64,out_channels=128, block_num=2) self.block3 = self._make_layers(in_channels=128,out_channels=256, block_num=8) self.block4 = self._make_layers(in_channels=256,out_channels=512, block_num=8) self.block5 = self._make_layers(in_channels=512,out_channels=1024, block_num=4) self.avg_pool = nn.AvgPool2d(kernel_size=8,stride=1) self.linear = nn.Linear(in_features=1024,out_features=num_classes) self.softmax = nn.Softmax(dim=1) def _make_layers(self, in_channels,out_channels, block_num): _layers = [] _layers.append(Conv3x3BNReLU(in_channels=in_channels, out_channels=out_channels, stride=2)) for _ in range(block_num): _layers.append(Residual(nchannels=out_channels)) return nn.Sequential(*_layers) def forward(self, x): x = self.first_conv(x) x = self.block1(x) x = self.block2(x) x = self.block3(x) x = self.block4(x) x = self.block5(x) x = self.avg_pool(x) x = x.view(x.size(0),-1) x = self.linear(x) out = self.softmax(x) return x model = Darknet53() print(model) input = torch.randn(1,3,256,256) out = model(input) print(out.shape)YOLOv3中的Darknet53的网络各层参数详解参考资料Pytorch实现Darknet-53:https://blog.csdn.net/qq_41979513/article/details/102680028Darknet53网络各层参数详解：https://blog.csdn.net/qq_40210586/article/details/106144197
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