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目标检测(物体检测, Object Detection) 专知荟萃

    • 入门学习

    • 进阶文章

    • 综述

    • Tutorial

    • 视频教程

    • 代码

    • 领域专家


入门学习

  1. 图像目标检测(Object Detection)原理与实现 (1-6)

    • [http://www.voidcn.com/article/p-xnjyqlkj-ua.html]

    • [http://www.voidcn.com/article/p-ypylfzuk-ua.html]

    • [http://www.voidcn.com/article/p-pfihszbt-ua.html]

    • [http://www.voidcn.com/article/p-hcvjcaqy-ua.html]

    • [http://www.voidcn.com/article/p-kjogyjfz-ua.html]

    • [http://www.voidcn.com/article/p-zqfjjomb-u.html]

  2. . 基于特征共享的高效物体检测 Faster R-CNN和ResNet的作者任少卿 博士毕业论文 中文

    • [https://pan.baidu.com/s/1gfxTbNl]

  3. R-CNN:论文笔记

    • [http://www.cnblogs.com/kingstrong/p/4969472.html], [http://blog.gater.vip/articles/2015/11/02/1478607351098.html]

  4. Fast-RCNN:

    • 深度学习物体检测(三)——FAST-RCNN:

    • [http://www.itwendao.com/article/detail/374785.html]

    • Fast-RCNN:[https://zhuanlan.zhihu.com/p/24780395]

  5. Faster-RCNN:

    • [http://blog.csdn.net/zy1034092330/article/details/62044941]

  6. FPN:

    • Feature Pyramid Networks for Object Detection 论文笔记:

    • [http://blog.csdn.net/jesse_mx/article/details/54588085]

    • CVPR 2017论文解读:特征金字塔网络FPN:

    • [http://www.sohu.com/a/159780794_465975]

    • FPN(feature pyramid networks)算法讲解:

    • [http://blog.csdn.net/u014380165/article/details/72890275]

  7. R-FCN:

    • 基于区域的全卷积网络来检测物体:

    • [http://blog.csdn.net/shadow_guo/article/details/51767036]

    • [译] 基于R-FCN的物体检测:

    • [http://www.jianshu.com/p/db1b74770e52]

  8. SSD:

    • Single Shot MultiBox Detector论文阅读:

    • [http://blog.csdn.net/u010167269/article/details/52563573]

    • 【深度学习:目标检测】RCNN学习笔记(10):SSD:Single Shot MultiBox Detector:

    • [http://blog.csdn.net/smf0504/article/details/52745070]

    • 翻译SSD论文(Single Shot MultiBox Detector),仅作交流:

    • [http://blog.csdn.net/Ai_Smith/article/details/52997456?locationNum=2&fps=1]

    • CNN目标检测与分割(三):SSD详解:

    • [http://blog.csdn.net/zy1034092330/article/details/72862030]

    • SSD关键源码解析:

    • [https://zhuanlan.zhihu.com/p/25100992]

  9. YOLO:

    • YOLO:实时快速目标检测:

    • [https://zhuanlan.zhihu.com/p/25045711]

    • YOLO详解: [https://zhuanlan.zhihu.com/p/25236464]

    • YOLO升级版:YOLOv2和YOLO9000解析:

    • [https://zhuanlan.zhihu.com/p/29816334]

    • YOLO升级版:YOLOv2和YOLO9000解析:

    • [https://zhuanlan.zhihu.com/p/29816334]

    • YOLO v2之总结篇(linux+windows):

    • [http://blog.csdn.net/qq_14845119/article/details/53589282]

    • YOLOv2 论文笔记:

    • [http://blog.csdn.net/jesse_mx/article/details/53925356]

  10. DenseBox:余凯特邀报告:基于密集预测图的物体检测技术造就全球领先的ADAS系统

  11. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - [http://www.cnblogs.com/xueyuxiaolang/p/5959442.html]

  12. 深度学习论文笔记:DSSD - [http://jacobkong.github.io/posts/2938514597/]

  13. DSOD

    • 复旦大学Ph.D沈志强:用于目标检测的DSOD模型(ICCV 2017) | 分享总结:

    • [http://www.sohu.com/a/198226907_114877]

    • 目标检测--DSOD: Learning Deeply Supervised Object Detectors from Scratch:

    • [http://blog.csdn.net/zhangjunhit/article/details/77247695]

  14. Focal Loss:

    • Focal Loss:

    • [http://blog.csdn.net/u014380165/article/details/77019084]

    • 读Focal Loss:

    • [https://zhuanlan.zhihu.com/p/28873248]

  15. Soft-NMS:

    • 一行代码改进NMS:

    • [http://blog.csdn.net/shuzfan/article/details/71036040]

  16. OHEM:

    • 论文笔记 OHEM: Training Region-based Object Detectors with Online Hard Example Mining:

    • [http://blog.csdn.net/u012905422/article/details/52760669]

  17. Mask-RCNN 2017:

    • Mask-RCNN 2017:

    • [http://blog.csdn.net/inuchiyo_china/article/details/70860939]

    • 目标检测分割--Mask R-CNN:

    • [http://blog.csdn.net/zhangjunhit/article/details/64920075?locationNum=6&fps=1]

    • 解读|Facebook 何凯明发大招:Mask R-CNN 狙击目标实例分割:

    • [http://www.sohu.com/a/130676187_642762]

  18. 目标检测之比较

    • 目标检测之RCNN,SPP-NET,Fast-RCNN,Faster-RCNN:

    • [http://lanbing510.info/2017/08/24/RCNN-FastRCNN-FasterRCNN.html]

    • RCNN, Fast-RCNN, Faster-RCNN的一些事:

    • [http://closure11.com/rcnn-fast-rcnn-faster-rcnn%E7%9A%84%E4%B8%80%E4%BA%9B%E4%BA%8B/]

    • 机器视觉目标检测补习贴之R-CNN系列 — R-CNN, Fast R-CNN, Faster R-CNN , 目标检测补习贴之YOLO实时检测, You only look once :

    • [http://nooverfit.com/wp/]

    • 目标检测算法:RCNN、YOLO vs DPM:

    • [https://juejin.im/entry/59564e1f6fb9a06b9c7408f9]

    • 如何评价rcnn、fast-rcnn和faster-rcnn这一系列方法?:

    • [https://www.zhihu.com/question/35887527]

  19. 视觉目标检测和识别之过去,现在及可能

    • [https://zhuanlan.zhihu.com/p/27546796]


进阶文章

  1. Deep Neural Networks for Object Detection (基于DNN的对象检测)NIPS2013:

    • [https://cis.temple.edu/~yuhong/teach/2014_spring/papers/NIPS2013_DNN_OD.pdf]

  2. R-CNN Rich feature hierarchies for accurate object detection and semantic segmentation:

    • [https://arxiv.org/abs/1311.2524]

  3. Fast R-CNN :

    • [http://arxiv.org/abs/1504.08083]

  4. Faster R-CNN Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks:

    • [http://arxiv.org/abs/1506.01497]

  5. Scalable Object Detection using Deep Neural Networks

    • [http://arxiv.org/abs/1312.2249]

  6. Scalable, High-Quality Object Detection

    • [http://arxiv.org/abs/1412.1441]

  7. SPP-Net Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

    • [http://arxiv.org/abs/1406.4729]

  8. DeepID-Net DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

    • [http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html]

  9. Object Detectors Emerge in Deep Scene CNNs

    • [http://arxiv.org/abs/1412.6856]

  10. segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

    • [https://arxiv.org/abs/1502.04275]

  11. Object Detection Networks on Convolutional Feature Maps

    • [http://arxiv.org/abs/1504.06066]

  12. Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

    • [http://arxiv.org/abs/1504.03293]

  13. DeepBox: Learning Objectness with Convolutional Networks

    • [http://arxiv.org/abs/1504.03293]

  14. Object detection via a multi-region & semantic segmentation-aware CNN model

    • [http://arxiv.org/abs/1505.01749]

  15. You Only Look Once: Unified, Real-Time Object Detection

    • [http://arxiv.org/abs/1506.02640]

  16. YOLOv2 YOLO9000: Better, Faster, Stronger

    • [https://arxiv.org/abs/1612.08242]

  17. AttentionNet: Aggregating Weak Directions for Accurate Object Detection

    • [http://arxiv.org/abs/1506.07704]

  18. DenseBox: Unifying Landmark Localization with End to End Object Detection

    • [http://arxiv.org/abs/1509.04874]

  19. SSD: Single Shot MultiBox Detector

    • [http://arxiv.org/abs/1512.02325]

  20. DSSD : Deconvolutional Single Shot Detector

    • [https://arxiv.org/abs/1701.06659]

  21. G-CNN: an Iterative Grid Based Object Detector

    • [http://arxiv.org/abs/1512.07729]

  22. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

    • [http://arxiv.org/abs/1604.00600]

  23. A MultiPath Network for Object Detection

    • [http://arxiv.org/abs/1604.02135]

  24. R-FCN: Object Detection via Region-based Fully Convolutional Networks

    • [http://arxiv.org/abs/1605.06409]

  25. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

    • [http://arxiv.org/abs/1607.07155]

  26. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

    • [http://arxiv.org/abs/1608.08021]

  27. Feature Pyramid Networks for Object Detection

    • [https://arxiv.org/abs/1612.03144]

  28. Learning Chained Deep Features and Classifiers for Cascade in Object Detection

    • [https://arxiv.org/abs/1702.07054]

  29. DSOD: Learning Deeply Supervised Object Detectors from Scratch

    • [https://arxiv.org/abs/1708.01241]

  30. Focal Loss for Dense Object Detection  ICCV 2017 Best student paper award. Facebook AI Research

    • [https://arxiv.org/abs/1708.02002]

    1. Mask-RCNN 2017 ICCV 2017 Best paper award. Facebook AI Research

    2. http://arxiv.org/abs/1703.06870


综述

  1. 深度学习之 "物体检测" 方法梳理

    • [http://zhwhong.ml/2017/02/24/Detection-CNN/]

  2. 地平线黄李超开讲:深度学习和物体检测!:

    • [http://www.sohu.com/a/163460329_642762]

  3. 对话CVPR2016:目标检测新进展:

    • [https://zhuanlan.zhihu.com/p/21533724]

  4. 基于深度学习的目标检测技术演进:R-CNN、Fast R-CNN、Faster R-CNN:

    • [http://www.cnblogs.com/skyfsm/p/6806246.html]

  5. 基于深度学习的目标检测研究进展

  6. 讲堂干货No.1|山世光-基于深度学习的目标检测技术进展与展望


Tutorial

  1. CVPR'17 Tutorial Deep Learning for Objects and Scenes by Kaiming He Ross Girshick

    • [http://deeplearning.csail.mit.edu/]

  2. ICCV 2015 Tools for Efficient Object Detection

    • [http://mp7.watson.ibm.com/ICCV2015/ObjectDetectionICCV2015.html]

  3. Object Detection

    • [http://class.inrialpes.fr/tutorials/triggs-icvss1.pdf]

  4. Image Recognition and Object Detection : Part 1

    • [https://www.learnopencv.com/image-recognition-and-object-detection-part1/]

  5. R-CNN for Object Detection

    • [https://courses.cs.washington.edu/courses/cse590v/14au/cse590v_wk1_rcnn.pdf\]


视频教程

  1. cs231 第11讲 Detection and Segmentation 

    • PPT :[http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf\] 视频:[https://www.youtube.com/watch?v=nDPWywWRIRo&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv]

  2. Deep Learning for Instance-level Object Understanding by Ross Girshick.

    • PPT:[http://deeplearning.csail.mit.edu/instance_ross.pdf\]

    • 视频:[https://youtu.be/jHv37mKAhV4?t=2349]


代码

  1. R-CNN

    • [https://github.com/rbgirshick/rcnn]

  2. Fast R-CNN:

    • [https://github.com/rbgirshick/fast-rcnn]

    • github("Fast R-CNN in MXNet"): https://github.com/precedenceguo/mx-rcnn

    • github: https://github.com/mahyarnajibi/fast-rcnn-torch

    • github: https://github.com/apple2373/chainer-simple-fast-rnn

    • github: https://github.com/zplizzi/tensorflow-fast-rcnn

  3. Faster R-CNN

    • github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn

    • github: https://github.com/rbgirshick/py-faster-rcnn

    • github: https://github.com/mitmul/chainer-faster-rcnn

    • github: https://github.com/andreaskoepf/faster-rcnn.torch

    • github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch

    • github: https://github.com/smallcorgi/Faster-RCNN_TF

    • github: https://github.com/CharlesShang/TFFRCNN

    • github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus

    • github: https://github.com/yhenon/keras-frcnn

  4. SPP-Net

    • [https://github.com/ShaoqingRen/SPP_net\]

  5. YOLO

    • github: https://github.com/gliese581gg/YOLO_tensorflow

    • github: https://github.com/xingwangsfu/caffe-yolo

    • github: https://github.com/frankzhangrui/Darknet-Yolo

    • github: https://github.com/BriSkyHekun/py-darknet-yolo

    • github: https://github.com/tommy-qichang/yolo.torch

    • github: https://github.com/frischzenger/yolo-windows

    • github: https://github.com/AlexeyAB/yolo-windows

    • github: https://github.com/nilboy/tensorflow-yolo

  6. YOLOv2

    • github(Chainer): https://github.com/leetenki/YOLOv2

    • github(Keras): https://github.com/allanzelener/YAD2K

    • github(PyTorch): https://github.com/longcw/yolo2-pytorch

    • github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow

    • github(Windows): https://github.com/AlexeyAB/darknet

    • github: https://github.com/choasUp/caffe-yolo9000

    • github: https://github.com/philipperemy/yolo-9000

  7. SSD

    • github: https://github.com/zhreshold/mxnet-ssd

    • github: https://github.com/zhreshold/mxnet-ssd.cpp

    • github: https://github.com/rykov8/ssd_keras

    • github: https://github.com/balancap/SSD-Tensorflow

    • github: https://github.com/amdegroot/ssd.pytorch

    • github(Caffe): https://github.com/chuanqi305/MobileNet-SSD

  8. Recurrent Scale Approximation for Object Detection in CNN

    • [https://github.com/sciencefans/RSA-for-object-detection]

  9. Mask-RCNN 2017

    • Keras [https://github.com/matterport/Mask_RCNN\]

    • TensorFlow [https://github.com/CharlesShang/FastMaskRCNN]

    • Pytorch [https://github.com/felixgwu/mask_rcnn_pytorch\]

    • caffe [https://github.com/jasjeetIM/Mask-RCNN]

    • MXNet [https://github.com/TuSimple/mx-maskrcnn]


领域专家

  1. Ross Girshick (rbg 大神)

    • [http://www.rossgirshick.info/]

  2. Kaiming He, Facebook人工智能实验室科学家Kaiming He

    • [http://kaiminghe.com/]

  3. Shaoqing Ren

    • [http://shaoqingren.com/]

  4. Jian Sun

    • [http://www.jiansun.org/]


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