原地址：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

Object Detection

Published: 09 Oct 2015 Category:

Method	VOC2007	VOC2010	VOC2012	ILSVRC 2013
OverFeat	24.3%
R-CNN (AlexNet)	58.5%	53.7%	53.3%	31.4%
R-CNN (VGG16)	66.0%
SPP_net(ZF-5)	54.2%(1-model), 60.9%(2-model)	31.84%(1-model), 35.11%(6-model)
DeepID-Net	64.1%	50.3%
NoC	73.3%	68.8%
Fast-RCNN (VGG16)	70.0%	68.8%	68.4%	19.7%(@[0.5-0.95]), 35.9%(@0.5)
MR-CNN	78.2%	73.9%
Faster-RCNN (VGG16)	78.8%	75.9%	21.9%(@[0.5-0.95]), 42.7%(@0.5)	198ms
Faster-RCNN (ResNet-101)	85.6%	83.8%	37.4%(@[0.5-0.95]), 59.0%(@0.5)
YOLO	63.4%	57.9%	45 fps
YOLO VGG-16	66.4%	21 fps
YOLOv2 544 × 544	78.6%	73.4%	21.6%(@[0.5-0.95]), 44.0%(@0.5)	40 fps
SSD300 (VGG16)	77.2%	75.8%	25.1%(@[0.5-0.95]), 43.1%(@0.5)	46 fps
SSD512 (VGG16)	79.8%	78.5%	28.8%(@[0.5-0.95]), 48.5%(@0.5)	19 fps
ION	79.2%	76.4%
CRAFT	75.7%	71.3%	48.5%
OHEM	78.9%	76.3%	25.5%(@[0.5-0.95]), 45.9%(@0.5)
R-FCN (ResNet-50)	77.4%	0.12sec(K40), 0.09sec(TitianX)
R-FCN (ResNet-101)	79.5%	0.17sec(K40), 0.12sec(TitianX)
R-FCN (ResNet-101),multi sc train	83.6%	82.0%	31.5%(@[0.5-0.95]), 53.2%(@0.5)
PVANet 9.0	89.8%	84.2%	750ms(CPU), 46ms(TitianX)

Papers

Deep Neural Networks for Object Detection

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

Fast R-CNN

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

Faster R-CNN

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

R-CNN minus R

Faster R-CNN in MXNet with distributed implementation and data parallelization

Contextual Priming and Feedback for Faster R-CNN

An Implementation of Faster RCNN with Study for Region Sampling

Interpretable R-CNN

intro: North Carolina State University & Alibaba
keywords: AND-OR Graph (AOG)

Light-Head R-CNN

Light-Head R-CNN: In Defense of Two-Stage Object Detector

Cascade R-CNN

Cascade R-CNN: Delving into High Quality Object Detection

MultiBox

Scalable Object Detection using Deep Neural Networks

Scalable, High-Quality Object Detection

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

DeepID-Net

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

Object Detectors Emerge in Deep Scene CNNs

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

Object Detection Networks on Convolutional Feature Maps

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

DeepBox: Learning Objectness with Convolutional Networks

MR-CNN

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

YOLO

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

darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++

Start Training YOLO with Our Own Data

YOLO: Core ML versus MPSNNGraph

TensorFlow YOLO object detection on Android

intro: Real-time object detection on Android using the YOLO network with TensorFlow

Computer Vision in iOS – Object Detection

YOLOv2

YOLO9000: Better, Faster, Stronger

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

LightNet: Bringing pjreddie’s DarkNet out of the shadows

AttentionNet: Aggregating Weak Directions for Accurate Object Detection

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

SSD

SSD: Single Shot MultiBox Detector

What’s the diffience in performance between this new code you pushed and the previous code? #327

Enhancement of SSD by concatenating feature maps for object detection

DSSD

DSSD : Deconvolutional Single Shot Detector

Context-aware Single-Shot Detector

keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)

Feature-Fused SSD: Fast Detection for Small Objects

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

Inside-Outside Net (ION)

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Adaptive Object Detection Using Adjacency and Zoom Prediction

G-CNN: an Iterative Grid Based Object Detector

Factors in Finetuning Deep Model for object detection

Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution

We don’t need no bounding-boxes: Training object class detectors using only human verification

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

A MultiPath Network for Object Detection

CRAFT

CRAFT Objects from Images

OHEM

Training Region-based Object Detectors with Online Hard Example Mining

S-OHEM: Stratified Online Hard Example Mining for Object Detection

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

intro: CVPR 2016
keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)

R-FCN

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

R-FCN-3000 at 30fps: Decoupling Detection and Classification

Recycle deep features for better object detection

MS-CNN

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

Multi-stage Object Detection with Group Recursive Learning

Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection

PVANET

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

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021

GBD-Net

Gated Bi-directional CNN for Object Detection

Crafting GBD-Net for Object Detection

intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
intro: gated bi-directional CNN (GBD-Net)

StuffNet

StuffNet: Using ‘Stuff’ to Improve Object Detection

Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

Hierarchical Object Detection with Deep Reinforcement Learning

Learning to detect and localize many objects from few examples

Speed/accuracy trade-offs for modern convolutional object detectors

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

Feature Pyramid Network (FPN)

Feature Pyramid Networks for Object Detection

Action-Driven Object Detection with Top-Down Visual Attentions

Beyond Skip Connections: Top-Down Modulation for Object Detection

Wide-Residual-Inception Networks for Real-time Object Detection

Attentional Network for Visual Object Detection

intro: University of Maryland & Mitsubishi Electric Research Laboratories

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

keykwords: CC-Net
intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries

Spatial Memory for Context Reasoning in Object Detection

Accurate Single Stage Detector Using Recurrent Rolling Convolution

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection

LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc

Point Linking Network for Object Detection

Perceptual Generative Adversarial Networks for Small Object Detection

Few-shot Object Detection

Yes-Net: An effective Detector Based on Global Information

SMC Faster R-CNN: Toward a scene-specialized multi-object detector

Towards lightweight convolutional neural networks for object detection

RON: Reverse Connection with Objectness Prior Networks for Object Detection

Mimicking Very Efficient Network for Object Detection

Residual Features and Unified Prediction Network for Single Stage Detection

Deformable Part-based Fully Convolutional Network for Object Detection

intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC

Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

Recurrent Scale Approximation for Object Detection in CNN

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

Focal Loss for Dense Object Detection

intro: ICCV 2017 Best student paper award. Facebook AI Research
keywords: RetinaNet

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection

StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection

Dynamic Zoom-in Network for Fast Object Detection in Large Images

Zero-Annotation Object Detection with Web Knowledge Transfer

intro: NTU, Singapore & Amazon
keywords: multi-instance multi-label domain adaption learning framework

MegDet

MegDet: A Large Mini-Batch Object Detector

intro: Peking University & Tsinghua University & Megvii Inc

Single-Shot Refinement Neural Network for Object Detection

Receptive Field Block Net for Accurate and Fast Object Detection

An Analysis of Scale Invariance in Object Detection - SNIP

Feature Selective Networks for Object Detection

Learning a Rotation Invariant Detector with Rotatable Bounding Box

Scalable Object Detection for Stylized Objects

Relation Networks for Object Detection

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

Deep Regionlets for Object Detection

keywords: region selection network, gating network

Adversarial Examples that Fool Detectors

NMS

End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression

A convnet for non-maximum suppression

Improving Object Detection With One Line of Code

Soft-NMS – Improving Object Detection With One Line of Code

Learning non-maximum suppression

Weakly Supervised Object Detection

Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection

Weakly supervised object detection using pseudo-strong labels

Saliency Guided End-to-End Learning for Weakly Supervised Object Detection

Detection From Video

Learning Object Class Detectors from Weakly Annotated Video

Analysing domain shift factors between videos and images for object detection

Video Object Recognition

Deep Learning for Saliency Prediction in Natural Video

Object Detection目標檢測全面總結--重要