目标识别深度学习进行目标识别的资源列表

Posted 蚂蚁flow

tags:

篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了目标识别深度学习进行目标识别的资源列表相关的知识,希望对你有一定的参考价值。

【目标识别】深度学习进行目标识别的资源列表:O网页链接 包括RNN、MultiBox、SPP-Net、DeepID-Net、Fast R-CNN、DeepBox、MR-CNN、Faster R-CNN、YOLO、DenseBox、SSD、Inside-Outside Net、G-CNN等。
Papers

Deep Neural Networks for Object Detection
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

[td]

method
ILSVRC 2013 mAP
OverFeat
24.3%

R-CNN

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

[td]

method
VOC 2007 mAP
VOC 2010 mAP
VOC 2012 mAP
ILSVRC 2013 mAP
R-CNN,AlexNet
54.2%
50.2%
49.6%
 
R-CNN,bbox reg,AlexNet
58.5%
53.7%
53.3%
31.4%
R-CNN,bbox reg,ZFNet
59.2%
     
R-CNN,VGG-Net
62.2%
     
R-CNN,bbox reg,VGG-Net
66.0%
     

MultiBox

Scalable Object Detection using Deep Neural Networks (MultiBox)

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

[td]

method
VOC 2007 mAP
ILSVRC 2013 mAP
SPP_net(ZF-5),1-model
54.2%
31.84%
SPP_net(ZF-5),2-model
60.9%
 
SPP_net(ZF-5),6-model   35.11%
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Scalable, High-Quality Object Detection

DeepID-Net

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

[td]

method
VOC 2007 mAP
ILSVRC 2013 mAP
DeepID-Net
64.1%
50.3%
Object Detection Networks on Convolutional Feature Maps

[td]

method
Trained on
mAP
NoC
07+12
68.8%
NoC,bb
07+12
71.6%
NoC,+EB
07+12
71.8%
NoC,+EB,bb
07+12
73.3%
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

[td]

Model
BBoxReg?
VOC 2007 mAP(IoU>0.5)
R-CNN(AlexNet)
No
54.2%
R-CNN(VGG)
No
60.6%
+StructObj
No
61.2%
+StructObj-FT
No
62.3%
+FGS
No
64.8%
+StructObj+FGS
No
65.9%
+StructObj-FT+FGS
No
66.5%

[td]

Model
BBoxReg?
VOC 2007 mAP(IoU>0.5)
R-CNN(AlexNet)
Yes
58.5%
R-CNN(VGG)
Yes
65.4%
+StructObj
Yes
66.6%
+StructObj-FT
Yes
66.9%
+FGS
Yes
67.2%
+StructObj+FGS
Yes
68.5%
+StructObj-FT+FGS
Yes
68.4%

Fast R-CNN

Fast R-CNN

[td]

method
data
VOC 2007 mAP
FRCN,VGG16
07
66.9%
FRCN,VGG16
07+12
70.0%

[td]

method
data
VOC 2010 mAP
FRCN,VGG16
12
66.1%
FRCN,VGG16
07++12
68.8%

[td]

method
data
VOC 2012 mAP
FRCN,VGG16
12
65.7%
FRCN,VGG16
07++12
68.4%

DeepBox

DeepBox: Learning Objectness with Convolutional Networks

MR-CNN

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

[td]

Model
Trained on
VOC 2007 mAP
VGG-net
07+12
78.2%
VGG-net
07
74.9%

[td]

Model
Trained on
VOC 2012 mAP
VGG-net
07+12
73.9%
VGG-net
12
70.7%

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks(NIPS 2015)

[td]

  training data
test data
mAP
time/img
Faster RCNN, VGG-16
07
VOC 2007 test
69.9%
198ms
Faster RCNN, VGG-16
07+12
VOC 2007 test
73.2%
198ms
Faster RCNN, VGG-16
12
VOC 2007 test
67.0%
198ms
Faster RCNN, VGG-16
07++12
VOC 2007 test
70.4%
198ms

YOLO

You Only Look Once: Unified, Real-Time Object Detection(YOLO)
R-CNN minus R

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

SSD

SSD: Single Shot MultiBox Detector
技术分享

Inside-Outside Net

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Detection results on VOC 2007 test:

[td]

Method
R
S
W
D
Train
mAP
FRCN
       
07+12
70.0
RPN
       
07+12
73.2
MR-CNN
   
  07+12
78.2
ION
       
07+12
74.6
ION
      07+12
75.6
ION
   
07+12+S
76.5
ION
  07+12+S
78.5
ION
07+12+S
79.2
Detection results on VOC 2012 test:

[td]

Method
R
S
W
D
Train
mAP
FRCN
       
07++12
68.4
RPN
       
07++12
70.4
FRCN+YOLO
       
07++12
70.4
HyperNet
       
07++12
71.4
MR-CNN
   
  07+12
73.9
ION
07+12+S
76.4

G-CNN

G-CNN: an Iterative Grid Based Object Detector
Learning Deep Features for Discriminative Localization
Factors in Finetuning Deep Model for object detection
We don’t need no bounding-boxes: Training object class detectors using only human verification
A MultiPath Network for Object Detection
Beyond Bounding Boxes: Precise Localization of Objects in Images (PhD Thesis)
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
Training Region-based Object Detectors with Online Hard Example Mining

Specific Object Deteciton

End-to-end people detection in crowded scenes
技术分享

Tutorials

Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection

Codes

TensorBox: a simple framework for training neural networks to detect objects in images
Object detection in torch: Implementation of some object detection frameworks in torch

Blogs

Convolutional Neural Networks for Object Detection


以上是关于目标识别深度学习进行目标识别的资源列表的主要内容,如果未能解决你的问题,请参考以下文章

第十八节基于传统图像处理的目标检测与识别(HOG+SVM附代码)

目标跟踪的深度学习方法与opencv下的KCF方法

深度学习和目标检测系列教程 23-300 :FasterRCNN和yolov5训练飞机目标识别的小项目

车牌识别到人脸识别的图像识别路程

如何用 OpenCVPython 和深度学习实现面部识别?

精彩论文基于深度学习和图像识别的电力配件智能出入库