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目标检测之CNN系列

2017-09-07 14:41 381 查看

排行榜

pascal VOC:http://host.robots.ox.ac.uk:8080/leaderboard/main_bootstrap.php

coco:http://mscoco.org/dataset/#detections-leaderboard

kitti:http://www.cvlibs.net/datasets/kitti/eval_object.php

Regionlets for Generic Object
Detection
http://blog.csdn.net/maweifei/article/details/59078077 http://blog.csdn.net/maweifei/article/details/59078077
目标检测中的mAP是什么含义？

awesome-object-proposals

论文笔记之---Speed and accuracy trade-offs
for modern convolutional object detectors

cs231n学习笔记-CNN-目标检测、定位、分割

基于深度学习的目标检测

ILSVRC2016目标检测任务回顾：图像目标检测（DET）

overfeat

深度学习（二十）基于Overfeat的图片分类、定位、检测-2014 ICLR

使用sklearn-theano来做object detection目标检测 (OverFeat)

深度学习研究理解:OverFeat:Integrated Recognition, Localization and Detection using Convolutional
Networks

RCNN

读DL论文心得之RCNN

rcnn学习笔记:Rich feature hierarchies for accurate object detection and semantic segmentation

R-CNN论文详解

DeepID-Net

DeepID-Net:multi-stage and deformable
deep CNNs for object detection

selective search & Edge box

论文提要“Selective Search for Object Recognition”

《Selective Search for object recognition》阅读笔记

Selective
Search for Object Recognition(阅读)

What makes for effective detection proposals?

《Edge
Boxes: Locating Object Proposals from Edges》读后感~

Oriented
Object Proposals

其实·RPN也算一种

sppnet

SPPNet

论文笔记《Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition》

读DL论文心得之SPP

Spatial Pyramid Pooling in Deep Convolutional
--- Spp_net

深度学习笔记（一）空间金字塔池化阅读笔记Spatial Pyramid Pooling in
Deep Convolutional Networks for Visual Recognition

SPP pooling layer

深度学习（十九）基于空间金字塔池化的卷积神经网络物体检测-ECCV
2014

Spatial
pyramid pooling (SPP)-net （空间金字塔池化）笔记

Fast RCNN

【目标检测】Fast RCNN算法详解

读DL论文心得之Fast RCNN

论文提要"Fast R-CNN"

Fast R-CNN

论文笔记《Fast R-CNN》

深度学习入门（二）Fast R-CNN

Fast-rcnn Notes

Fast
R-CNN笔记

论文笔记 Fast R-CNN细节

Fast R-CNN论文详解

Faster RCNN

【目标检测】Faster RCNN算法详解

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

Faster R-CNN论文笔记——FR

RCNN & SPP-net & Fast-RCNN & Faster-RCNN

物体检测-从RCNN到YOLO

Region Proposal Networks

Faster R-CNN学习笔记

Faster rcnn test浮点运算次数（卷积实现过程，Faster rcnn总体结构和参数）

bounding box regression

Faster
R-CNN论文详解
feature map计算方法与faster-rcnn中roi映射到feature map的位置计算方法
Faster R-CNN
Faster RCNN-generate_anchors.py

faster-rcnn 之 RPN网络的结构解析

faster rcnn的特征图到原图区域映射问题

在CNN网络中roi从原图映射到feature map中的计算方法

针对Faster RCNN具体细节以及源码的解读之RoIPooling层

py-faster rcnn中rpn的3x3的滑框用卷积层来定义的是为什么？

Faster
rcnn相关文章研究

Faster R-CNN论文详解
http://wenku.baidu.com/link?url=11zlcxN9p7d6ydhJHnDCBltkS9HEyLLZ0sBBgdwq7Oa02BsXeKRIBvDWcPVzKMFV8SOBtc9qdhTilohJ55MhUc7Ht7jDUiCh4yhn5xvRUYMiXX2T9lzG2zdYF5PDuFtn
Region Proposal Network

Single Bounding Box Regression
http://blog.csdn.net/qq_26898461/article/category/6204814
CTPN: Detecting Text in Natural Image with Connectionist
Text Proposal Network

[目标检测] Faster R-CNN 深入理解 && 改进方法汇总

DeepBox

DeepBox: Learning Objectness with Convolutional
Networks

YOLO

论文阅读笔记：You
Only Look Once: Unified, Real-Time Object Detection

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

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

You Only Look Once: Unified, Real-Time
Object Detection
http://blog.csdn.net/u012235274/article/category/6204386
幾個關於 ssd 與 yolo 無法理解的地方

图解YOLO

YOLO2

YOLO详解

YOLOv2 论文笔记

yolo v2 训练

DenseBox

DenseBox: Unifying Landmark Localization
with End to End Object Detection

SSD

论文阅读：SSD: Single Shot MultiBox Detector
http://blog.csdn.net/u012235274/article/category/6366885
为什么SSD要比YOLO快？

ssd:single shot detector中的几个问题

SSD: Single Shot MultiBox Detector

基于深度学习的目标检测DET - SSD

SSD

DSSD: Deconvolutional Single Shot Detector 论文笔记

G-CNN

论文笔记 G-CNN: an Iterative Grid Based Object Detector

MultiPathNet

论文笔记 A MultiPath Network for Object Detection
https://github.com/facebookresearch/multipathnet

HyperNet

论文笔记 HyperNet: Towards Accurate Region Proposal Generation
and Joint Object Detection

[论文阅读]HyperNet: Towards
Accurate Region Proposal Generation and Joint Object Detection

HyperNet: Towards Accurate Region Proposal
Generation and Joint Object Detection

LocNet

LocNet: Improving Localization Accuracy
for Object Detection

OHEM

论文笔记 Bootstrapping Face Detection with Hard Negative Examples

Training
Region-based Object Detectors with Online Hard Example Mining - cvpr 2016 oral

OHEM算法及Caffe代码详解
https://github.com/abhi2610/ohem
OHEM算法的Caffe实现

R-FCN

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

R-FCN：基于区域的全卷积网络来检测物体

R-FCN

[论文阅读]R-FCN: Object Detection via Region-based Fully Convolutional Networks

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

R-FCN: Object Detection via Region-based Fully Convolutional
Networks
https://github.com/Orpine/py-R-FCN

MS-CNN

论文笔记 MSCNN：A Unified Multi-scale Deep Convolutional Neural
Network for Fast Object Detection
https://github.com/zhaoweicai/mscnn

PVANET

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

PVANET: Deep but Lightweight Neural
Networks for Real-time Object Detection - arxiv 2016.08

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

FPN

目标检测--Feature Pyramid Networks for
Object Detection

FPN + DSSD 阅读笔记

FPN（feature pyramid networks）算法讲解

Feature Pyramid
Networks for Object Detection 论文笔记

RRC

目标检测--Accurate Single Stage Detector
Using Recurrent Rolling Convolution

Accurate Single Stage Detector Using Recurrent Ring Convolution

Object Detection方法汇总

R-CNN,SPP-NET, Fast-R-CNN,Faster-R-CNN, YOLO, SSD系列深度学习检测方法梳理

CVPR2016目标检测之识别效率篇：YOLO, G-CNN, Loc-Net

CVPR2016目标检测之识别精度篇：ResNet, ION, HyperNet，R-FCN

深度学习检测方法梳理

目标检测论文回顾

对话CVPR2016：目标检测新进展

基于深度学习的目标检测新作

【目标识别】深度学习进行目标识别的资源列表
【深度学习论文笔记】Deep
Neural Networks for Object Detection

深度卷积神经网络在目标检测中的进展

目标检测“DPMs are CNNs”

视频目标检测 - Object Detection from Video
Tubelets with Convolutional Neural Networks

目标检测“Exploit All the Layers: Fast
and Accurate CNN Object Detector with Scale Dependent Pooling and”

Exploit
All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling 之再阅读

车辆检测”Learning to Detect Vehicles by
Clustering Appearance Patterns“

Fast detection of multiple objects
in traffic scenes with a common detection framework

3D Object Proposals for Accurate Object
Class Detection

车辆检测“Integrating Context and Occlusion for
Car Detection by Hierarchical And-Or Model”

论文提要“Filtered Channel Features for Pedestrian
Detection”

论文提要“Taking a Deeper Look at Pedestrians”

论文提要“Pedestrian Detection aided by Deep
Learning Semantic Tasks”

论文提要“Fast Feature Pyramids for Object Detection”

目标检测“Object Detection Using Generalization
and Efficiency Balanced Co-occurrence Features”

BING

BING++: A Fast High Quality Object
Proposal Generator at 100fps

综述：计算机视觉中RNN应用于目标识别
http://blog.csdn.net/zhangjunhit/article/category/6647655
Is Faster R-CNN Doing Well for Pedestrian Detection?

论文 Is Faster R-CNN Doing Well for Pedestrian Detection?探讨在行人检测领域Faster R-CNN是否有效，提出了RPN + Boosted Forest分类器。

基于R-CNN的多尺度改进方法概述

梳理基于R-CNN的多尺度改进方法，主要思路是提取多个层的feature进行卷积层的特征融合(即skip connections),涉及的方法有MultiPath Network，ION(Inside-Outside Net)，HyperNet，PVANET及MS-CNN。

多尺度R-CNN论文笔记(1): A MultiPath Network for Object Detection

多尺度R-CNN论文笔记(2): Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

多尺度R-CNN论文笔记(3): HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

多尺度R-CNN论文笔记(4): PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection

多尺度R-CNN论文笔记(5): A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

多尺度R-CNN论文笔记(6): Feature Pyramid Networks for Object Detection

Beyond
Skip Connections: Top-Down Modulation for Object Detection阅读笔记

无proposal，基于回归的检测算法概述

开创性工作YOLO与后续改进，以及G-CNN，SSD等工作。

无proposal检测方法(1): You Only Look Once: Unified, Real-Time Object Detection

无proposal检测方法(2): G-CNN: an Iterative Grid Based Object Detector

无proposal检测方法(3): SSD: Single Shot MultiBox Detector

YOLO9000: Better,Faster,Stronger（YOLO9000：更好，更快，更强）

和GAN结合

目标检测“A-Fast-RCNN: Hard Positive Generation
via Adversary for Object Detection”

A-Fast-RCNN算法的Caffe实现

A-Fast-RCNN 论文笔记

弱监督/无监督

目标定位--Deep Self-Taught Learning for Weakly
Supervised Object Localization

Is object localization for free? – Weakly Supervised Object Recognition
with Convolutional Neural Networks

Weakly Supervised Deep Detection Networks

Weakly supervised localization of novel objects using appearance transfer

PASCAL VOC数据集分析

VOC2007中包含9963张标注过的图片，由train/val/test三部分组成，共标注出24,640个物体。

VOC2007的test数据label已经公布，之后的没有公布（只有图片，没有label）。

对于检测任务，VOC2012的trainval/test包含08-11年的所有对应图片。 trainval有11540张图片共27450个物体。

对于分割任务， VOC2012的trainval包含07-11年的所有对应图片， test只包含08-11。trainval有 2913张图片共6929个物体。

Pascal VOC 数据集介绍

VOC数据集mAP计算

Faster-R-CNN（Python）.2：COCO数据集annotation内容

Bounding Box label code

Udacity
Self-Driving 目标检测数据集简介与使用

图片标注工具LabelImg使用教程