目标检测汇总

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
    • [http://mp.weixin.qq.com/s/9Z9mO3DJ7uPuirMAbPkDcw]
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
   • [https://mp.weixin.qq.com/s/RxogMJ86FkQHrzaF4SxFNQ]
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
   • [https://mp.weixin.qq.com/s/NV2hWofOCractLt45-wI1A]

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/]
5. phd，zdz