Object Detection(目标检测神文)

已于 2025-04-04 17:47:29 修改
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分类专栏： # 机器视觉大会（CVPR&ICCV&ECCV） 文章标签： 目标检测 目标跟踪 人工智能
于 2018-11-02 09:26:11 首次发布
原文链接：https://mpbeta.youkuaiyun.com/mp_blog/creation/editor/83650415

目标检测神文，非常全而且持续在更新。转发自：Object Detection - handong1587，如有侵权联系删除。
更新时间：
20181026

我会跟进原作者博客持续更新，加入自己对目标检测领域的一些新研究及论文解读。博客根据需求直接进行关键字搜索，例如2018，可找到最新论文。

文章目录

Methodbackbonetest sizeVOC2007VOC2010VOC2012ILSVRC 2013MSCOCO 2015Speed
OverFeat24.3%
R-CNNAlexNet58.5%53.7%53.3%31.4%
R-CNNVGG1766.0%
SPP_netZF-554.2%31.84%
DeepID-Net64.1%50.3%
NoC73.3%68.8%
Fast-RCNNVGG1670.0%68.8%68.4%19.7%(@[0.5-0.95]), 35.9%(@0.5)
MR-CNN78.2%73.9%
Faster-RCNNVGG1678.8%75.9%21.9%(@[0.5-0.95]), 42.7%(@0.5)198ms
Faster-RCNNResNet10185.6%83.8%37.4%(@[0.5-0.95]), 59.0%(@0.5)
YOLO63.4%57.9%45 fps
YOLOVGG-1666.4%21 fps
YOLOv2448x44878.6%73.4%21.6%(@[0.5-0.95]), 44.0%(@0.5)40 fps
SSDVGG16300x30077.2%75.8%25.1%(@[0.5-0.95]), 43.1%(@0.5)46 fps
SSDVGG16512x51279.8%78.5%28.8%(@[0.5-0.95]), 48.5%(@0.5)19 fps
SSDResNet101300x30028.0%(@[0.5-0.95])16 fps
SSDResNet101512x51231.2%(@[0.5-0.95])8 fps
DSSDResNet101300x30028.0%(@[0.5-0.95])8 fps
DSSDResNet101500x50033.2%(@[0.5-0.95])6 fps
ION79.2%76.4%
CRAFT75.7%71.3%48.5%
OHEM78.9%76.3%25.5%(@[0.5-0.95]), 45.9%(@0.5)
R-FCNResNet5077.4%0.12sec(K40), 0.09sec(TitianX)
R-FCNResNet10179.5%0.17sec(K40), 0.12sec(TitianX)
R-FCN(ms train)ResNet10183.6%82.0%31.5%(@[0.5-0.95]), 53.2%(@0.5)
PVANet 9.084.9%84.2%750ms(CPU), 46ms(TitianX)
RetinaNetResNet101-FPN
Light-Head R-CNNXception*800/120031.5%@[0.5:0.95]95 fps
Light-Head R-CNNXception*700/110030.7%@[0.5:0.95]102 fps

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

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

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: 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

这里写图片描述

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

这里写图片描述

YOLO: Core ML versus MPSNNGraph

TensorFlow YOLO object detection on Android

Computer Vision in iOS – Object Detection

YOLOv2

YOLO9000: Better, Faster, Stronger

darknet_scripts

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

LightNet: Bringing pjreddie’s DarkNet out of the shadows

YOLO v2 Bounding Box Tool

YOLOv3

YOLOv3: An Incremental Improvement

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

这里写图片描述

DSSD

DSSD : Deconvolutional Single Shot Detector

Enhancement of SSD by concatenating feature maps for object detection

Context-aware Single-Shot Detector

Feature-Fused SSD: Fast Detection for Small Objects

[1709.05054] Feature-Fused SSD: Fast Detection for Small Objects

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

[1712.00960] FSSD: Feature Fusion Single Shot Multibox Detector

Weaving Multi-scale Context for Single Shot Detector

ESSD

Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects

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)
  • paper: U-M Web Hosting

R-FCN

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

arxiv: [1605.06409] R-FCN: Object Detection via Region-based Fully Convolutional Networks
github: https://github.com/daijifeng001/R-FCN
github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn
github: https://github.com/Orpine/py-R-FCN
github: https://github.com/PureDiors/pytorch_RFCN
github: https://github.com/bharatsingh430/py-R-FCN-multiGPU
github: https://github.com/xdever/RFCN-tensorflow

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: Lightweight Deep Neural Networks for Real-time Object Detection

GBD-Net

Gated Bi-directional CNN for Object Detection

Crafting GBD-Net for Object Detection

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

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

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

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

[1707.05031] Residual Features and Unified Prediction Network for Single Stage Detection

Deformable Part-based Fully Convolutional Network for Object Detection

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

这里写图片描述

Object Detection from Scratch with Deep Supervision

##RetinaNet

Focal Loss for Dense Object Detection

Focal Loss Dense Detector for Vehicle Surveillance

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

Incremental Learning of Object Detectors without Catastrophic Forgetting

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

[1711.05187] Dynamic Zoom-in Network for Fast Object Detection in Large Images

Zero-Annotation Object Detection with Web Knowledge Transfer

MegDet

MegDet: A Large Mini-Batch Object Detector

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

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

Deep Regionlets for Object Detection

Training and Testing Object Detectors with Virtual Images

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video

Spot the Difference by Object Detection

Localization-Aware Active Learning for Object Detection

Object Detection with Mask-based Feature Encoding

LSTD: A Low-Shot Transfer Detector for Object Detection

Domain Adaptive Faster R-CNN for Object Detection in the Wild

Pseudo Mask Augmented Object Detection

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

Learning Region Features for Object Detection

Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection

Object Detection for Comics using Manga109 Annotations

Task-Driven Super Resolution: Object Detection in Low-resolution Images

Transferring Common-Sense Knowledge for Object Detection

Multi-scale Location-aware Kernel Representation for Object Detection

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

DetNet: A Backbone network for Object Detection

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

AdvDetPatch: Attacking Object Detectors with Adversarial Patches

Attacking Object Detectors via Imperceptible Patches on Background

Physical Adversarial Examples for Object Detectors

Quantization Mimic: Towards Very Tiny CNN for Object Detection

Object detection at 200 Frames Per Second

Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images

SNIPER: Efficient Multi-Scale Training

Soft Sampling for Robust Object Detection

MetaAnchor: Learning to Detect Objects with Customized Anchors

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Auto-Context R-CNN

Pooling Pyramid Network for Object Detection

Modeling Visual Context is Key to Augmenting Object Detection Datasets

Dual Refinement Network for Single-Shot Object Detection

Acquisition of Localization Confidence for Accurate Object Detection

CornerNet: Detecting Objects as Paired Keypoints

Unsupervised Hard Example Mining from Videos for Improved Object Detection

SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection

A Survey of Modern Object Detection Literature using Deep Learning

Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

Deep Feature Pyramid Reconfiguration for Object Detection

MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection

Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

Deep Learning for Generic Object Detection: A Survey

Non-Maximum Suppression (NMS)

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

A convnet for non-maximum suppression

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

Learning non-maximum suppression

Relation Networks for Object Detection

Adversarial Examples

Adversarial Examples that Fool Detectors

Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

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

Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Video Object Detection

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

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

Object Detection from Video Tubelets with Convolutional Neural Networks

Object Detection in Videos with Tubelets and Multi-context Cues

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

CNN Based Object Detection in Large Video Images

  • intro: WangTao @ 爱奇艺
  • keywords: object retrieval, object detection, scene classification
  • slides: NVIDIA On-Demand

Object Detection in Videos with Tubelet Proposal Networks

Flow-Guided Feature Aggregation for Video Object Detection

Video Object Detection using Faster R-CNN

Improving Context Modeling for Video Object Detection and Tracking

http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf

Temporal Dynamic Graph LSTM for Action-driven Video Object Detection

Mobile Video Object Detection with Temporally-Aware Feature Maps

Towards High Performance Video Object Detection

Impression Network for Video Object Detection

Spatial-Temporal Memory Networks for Video Object Detection

3D-DETNet: a Single Stage Video-Based Vehicle Detector

Object Detection in Videos by Short and Long Range Object Linking

Object Detection in Video with Spatiotemporal Sampling Networks

Towards High Performance Video Object Detection for Mobiles

Optimizing Video Object Detection via a Scale-Time Lattice

Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing

Object Detection on Mobile Devices

Pelee: A Real-Time Object Detection System on Mobile Devices

Object Detection in 3D

Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Focal Loss in 3D Object Detection

Object Detection on RGB-D

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Differential Geometry Boosts Convolutional Neural Networks for Object Detection

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

Zero-Shot Object Detection

Zero-Shot Detection

Zero-Shot Object Detection

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

Zero-Shot Object Detection by Hybrid Region Embedding

Salient Object Detection

This task involves predicting the salient regions of an image given by human eye fixations.

Best Deep Saliency Detection Models (CVPR 2016 & 2015)

Large-scale optimization of hierarchical features for saliency prediction in natural images

Predicting Eye Fixations using Convolutional Neural Networks

Saliency Detection by Multi-Context Deep Learning

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

Shallow and Deep Convolutional Networks for Saliency Prediction

Recurrent Attentional Networks for Saliency Detection

Two-Stream Convolutional Networks for Dynamic Saliency Prediction

Unconstrained Salient Object Detection

Unconstrained Salient Object Detection via Proposal Subset Optimization

这里写图片描述

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

Salient Object Subitizing

这里写图片描述

Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

A Deep Multi-Level Network for Saliency Prediction

Visual Saliency Detection Based on Multiscale Deep CNN Features

A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection

Deeply supervised salient object detection with short connections

Weakly Supervised Top-down Salient Object Detection

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

Visual Saliency Prediction Using a Mixture of Deep Neural Networks

A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network

Saliency Detection by Forward and Backward Cues in Deep-CNNs

Supervised Adversarial Networks for Image Saliency Detection

Group-wise Deep Co-saliency Detection

Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

Learning Uncertain Convolutional Features for Accurate Saliency Detection

Deep Edge-Aware Saliency Detection

Self-explanatory Deep Salient Object Detection

PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection

DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets

Recurrently Aggregating Deep Features for Salient Object Detection

Deep saliency: What is learnt by a deep network about saliency?

Contrast-Oriented Deep Neural Networks for Salient Object Detection

Salient Object Detection by Lossless Feature Reflection

HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection

Video Saliency Detection

Deep Learning For Video Saliency Detection

Video Salient Object Detection Using Spatiotemporal Deep Features

Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM

Visual Relationship Detection

Visual Relationship Detection with Language Priors

ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

Visual Translation Embedding Network for Visual Relation Detection

Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

Detecting Visual Relationships with Deep Relational Networks

Identifying Spatial Relations in Images using Convolutional Neural Networks

PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN

Natural Language Guided Visual Relationship Detection

Detecting Visual Relationships Using Box Attention

Google AI Open Images - Visual Relationship Track

Context-Dependent Diffusion Network for Visual Relationship Detection

A Problem Reduction Approach for Visual Relationships Detection

Face Deteciton

Multi-view Face Detection Using Deep Convolutional Neural Networks

From Facial Parts Responses to Face Detection: A Deep Learning Approach

Compact Convolutional Neural Network Cascade for Face Detection

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

Towards a Deep Learning Framework for Unconstrained Face Detection

Supervised Transformer Network for Efficient Face Detection

UnitBox: An Advanced Object Detection Network

Bootstrapping Face Detection with Hard Negative Examples

Grid Loss: Detecting Occluded Faces

A Multi-Scale Cascade Fully Convolutional Network Face Detector

MTCNN

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks

这里写图片描述

Face Detection using Deep Learning: An Improved Faster RCNN Approach

Faceness-Net: Face Detection through Deep Facial Part Responses

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”

End-To-End Face Detection and Recognition

Face R-CNN

Face Detection through Scale-Friendly Deep Convolutional Networks

Scale-Aware Face Detection

Detecting Faces Using Inside Cascaded Contextual CNN

Multi-Branch Fully Convolutional Network for Face Detection

SSH: Single Stage Headless Face Detector

Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

S3FD: Single Shot Scale-invariant Face Detector

Detecting Faces Using Region-based Fully Convolutional Networks

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

Face Attention Network: An effective Face Detector for the Occluded Faces

Feature Agglomeration Networks for Single Stage Face Detection

Face Detection Using Improved Faster RCNN

PyramidBox: A Context-assisted Single Shot Face Detector

A Fast Face Detection Method via Convolutional Neural Network

Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy

Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks

SFace: An Efficient Network for Face Detection in Large Scale Variations

Survey of Face Detection on Low-quality Images

Anchor Cascade for Efficient Face Detection

Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization

Selective Refinement Network for High Performance Face Detection

Detect Small Faces

Finding Tiny Faces

Detecting and counting tiny faces

Seeing Small Faces from Robust Anchor’s Perspective

Face-MagNet: Magnifying Feature Maps to Detect Small Faces

Person Head Detection

Context-aware CNNs for person head detection

Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture

A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

FCHD: A fast and accurate head detector

Pedestrian Detection / People Detection

Pedestrian Detection aided by Deep Learning Semantic Tasks

Deep Learning Strong Parts for Pedestrian Detection

Taking a Deeper Look at Pedestrians

Convolutional Channel Features

End-to-end people detection in crowded scenes

Learning Complexity-Aware Cascades for Deep Pedestrian Detection

Deep convolutional neural networks for pedestrian detection

Scale-aware Fast R-CNN for Pedestrian Detection

New algorithm improves speed and accuracy of pedestrian detection

Pushing the Limits of Deep CNNs for Pedestrian Detection

A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation

Is Faster R-CNN Doing Well for Pedestrian Detection?

Unsupervised Deep Domain Adaptation for Pedestrian Detection

Reduced Memory Region Based Deep Convolutional Neural Network Detection

Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection

Detecting People in Artwork with CNNs

Multispectral Deep Neural Networks for Pedestrian Detection

Deep Multi-camera People Detection

Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

What Can Help Pedestrian Detection?

Illuminating Pedestrians via Simultaneous Detection & Segmentation

Rotational Rectification Network for Robust Pedestrian Detection

STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos

Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy

Repulsion Loss: Detecting Pedestrians in a Crowd

Aggregated Channels Network for Real-Time Pedestrian Detection

Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

PCN: Part and Context Information for Pedestrian Detection with CNNs

Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation

Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation

Vehicle Detection

DAVE: A Unified Framework for Fast Vehicle Detection and Annotation

Evolving Boxes for fast Vehicle Detection

Fine-Grained Car Detection for Visual Census Estimation

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data

Traffic-Sign Detection

Traffic-Sign Detection and Classification in the Wild

Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data

Detecting Small Signs from Large Images

Localized Traffic Sign Detection with Multi-scale Deconvolution Networks

Detecting Traffic Lights by Single Shot Detection

A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection

Skeleton Detection

Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs

这里写图片描述

DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images

SRN: Side-output Residual Network for Object Symmetry Detection in the Wild

Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

Fruit Detection

Deep Fruit Detection in Orchards

Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

Shadow Detection

Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network

A+D-Net: Shadow Detection with Adversarial Shadow Attenuation

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

Direction-aware Spatial Context Features for Shadow Detection

Direction-aware Spatial Context Features for Shadow Detection and Removal

Others Detection

Deep Deformation Network for Object Landmark Localization

Fashion Landmark Detection in the Wild

Deep Learning for Fast and Accurate Fashion Item Detection

OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)

这里写图片描述

Selfie Detection by Synergy-Constraint Based Convolutional Neural Network

Associative Embedding:End-to-End Learning for Joint Detection and Grouping

Deep Cuboid Detection: Beyond 2D Bounding Boxes

Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Deep Learning Logo Detection with Data Expansion by Synthesising Context

Scalable Deep Learning Logo Detection

Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks

Automatic Handgun Detection Alarm in Videos Using Deep Learning

Objects as context for part detection

Using Deep Networks for Drone Detection

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

Target Driven Instance Detection

DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition

Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants

ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos

Deep Learning Object Detection Methods for Ecological Camera Trap Data

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

Towards End-to-End Lane Detection: an Instance Segmentation Approach

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

Densely Supervised Grasp Detector (DSGD)

Object Proposal

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

Scale-aware Pixel-wise Object Proposal Networks

Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

Learning to Segment Object Proposals via Recursive Neural Networks

Learning Detection with Diverse Proposals

ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond

Improving Small Object Proposals for Company Logo Detection

Open Logo Detection Challenge

Localization

Beyond Bounding Boxes: Precise Localization of Objects in Images

Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning

Weakly Supervised Object Localization Using Size Estimates

Active Object Localization with Deep Reinforcement Learning

Localizing objects using referring expressions

LocNet: Improving Localization Accuracy for Object Detection

Learning Deep Features for Discriminative Localization

这里写图片描述

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

这里写图片描述

Ensemble of Part Detectors for Simultaneous Classification and Localization

STNet: Selective Tuning of Convolutional Networks for Object Localization

Soft Proposal Networks for Weakly Supervised Object Localization

Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

Tutorials / Talks

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

Towards Good Practices for Recognition & Detection

Work in progress: Improving object detection and instance segmentation for small objects

https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo/edit#slide=id.g37418adc7a_0_229

Object Detection with Deep Learning: A Review

Projects

Detectron

TensorBox: a simple framework for training neural networks to detect objects in images

  • intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
  • github: https://github.com/Russell91/TensorBox

Object detection in torch: Implementation of some object detection frameworks in torch

Using DIGITS to train an Object Detection network

FCN-MultiBox Detector

KittiBox: A car detection model implemented in Tensorflow.

Deformable Convolutional Networks + MST + Soft-NMS

How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow

Metrics for object detection

Leaderboard

Detection Results: VOC2012

Tools

BeaverDam: Video annotation tool for deep learning training labels

https://github.com/antingshen/BeaverDam

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-object-detection/

Introducing automatic object detection to visual search (Pinterest)

Deep Learning for Object Detection with DIGITS

Analyzing The Papers Behind Facebook’s Computer Vision Approach

Easily Create High Quality Object Detectors with Deep Learning

How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit

Object Detection in Satellite Imagery, a Low Overhead Approach

You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks

Faster R-CNN Pedestrian and Car Detection

Small U-Net for vehicle detection

Region of interest pooling explained

Supercharge your Computer Vision models with the TensorFlow Object Detection API

Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning

https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab

One-shot object detection

One-stage object detection

An overview of object detection: one-stage methods

An overview of object detection: one-stage methods.

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