【专知荟萃09】目标检测知识资料全集（入门/进阶/论文/综述/视频/代码等）

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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

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]

. 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]

R-CNN：论文笔记

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

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]

Faster-RCNN:

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

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]

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]

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]

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]

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]

PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - [http://www.cnblogs.com/xueyuxiaolang/p/5959442.html]
深度学习论文笔记：DSSD - [http://jacobkong.github.io/posts/2938514597/]
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]

Focal Loss:

Focal Loss:
[http://blog.csdn.net/u014380165/article/details/77019084]
读Focal Loss:
[https://zhuanlan.zhihu.com/p/28873248]

Soft-NMS:

一行代码改进NMS:
[http://blog.csdn.net/shuzfan/article/details/71036040]

OHEM:

论文笔记 OHEM: Training Region-based Object Detectors with Online Hard Example Mining:
[http://blog.csdn.net/u012905422/article/details/52760669]

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]

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]

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

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]

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

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

Fast R-CNN :

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

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

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

Scalable Object Detection using Deep Neural Networks

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

Scalable, High-Quality Object Detection

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

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

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

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

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

Object Detectors Emerge in Deep Scene CNNs

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

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

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

Object Detection Networks on Convolutional Feature Maps

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

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

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

DeepBox: Learning Objectness with Convolutional Networks

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

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

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

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

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

YOLOv2 YOLO9000: Better, Faster, Stronger

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

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

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

DenseBox: Unifying Landmark Localization with End to End Object Detection

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

SSD: Single Shot MultiBox Detector

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

DSSD : Deconvolutional Single Shot Detector

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

G-CNN: an Iterative Grid Based Object Detector

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

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

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

A MultiPath Network for Object Detection

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

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

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

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

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

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

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

Feature Pyramid Networks for Object Detection

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

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

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

DSOD: Learning Deeply Supervised Object Detectors from Scratch

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

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

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

Mask-RCNN 2017 ICCV 2017 Best paper award. Facebook AI Research
http://arxiv.org/abs/1703.06870

综述

深度学习之 "物体检测" 方法梳理

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

地平线黄李超开讲：深度学习和物体检测！：

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

对话CVPR2016：目标检测新进展：

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

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]

基于深度学习的目标检测研究进展

[https://mp.weixin.qq.com/s/RxogMJ86FkQHrzaF4SxFNQ]

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

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

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

ICCV 2015 Tools for Efficient Object Detection

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

Object Detection

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

Image Recognition and Object Detection : Part 1

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

R-CNN for Object Detection

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

视频教程

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]

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

R-CNN

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

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

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