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Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

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Created 2018-04-06 · Updated 2026-10-05 · #7162 today
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README

object-detection

[TOC]

This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to Date.

  • R-CNN
  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN
  • Light-Head R-CNN
  • Cascade R-CNN
  • SPP-Net
  • YOLO
  • YOLOv2
  • YOLOv3
  • YOLT
  • SSD
  • DSSD
  • FSSD
  • ESSD
  • MDSSD
  • Pelee
  • Fire SSD
  • R-FCN
  • FPN
  • DSOD
  • RetinaNet
  • MegDet
  • RefineNet
  • DetNet
  • SSOD
  • CornerNet
  • M2Det
  • 3D Object Detection
  • ZSD（Zero-Shot Object Detection）
  • OSD（One-Shot object Detection）
  • Weakly Supervised Object Detection
  • Softer-NMS
  • 2018
  • 2019
  • Other

Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

Survey

Imbalance Problems in Object Detection: A Review

  • intro: under review at TPAMI
  • arXiv:

Recent Advances in Deep Learning for Object Detection

  • intro: From 2013 (OverFeat) to 2019 (DetNAS)
  • arXiv:

A Survey of Deep Learning-based Object Detection

  • intro：From Fast R-CNN to NAS-FPN

  • arXiv：

Object Detection in 20 Years: A Survey

  • intro：This work has been submitted to the IEEE TPAMI for possible publication
  • arXiv：

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

《Deep Learning for Generic Object Detection: A Survey》

Papers&Codes

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

  • intro: R-CNN
  • arxiv:
  • supp:
  • slides:
  • slides:
  • github:
  • notes:
  • caffe-pr("Make R-CNN the Caffe detection example"):

Fast R-CNN

Fast R-CNN

  • arxiv:
  • slides:
  • github:
  • github(COCO-branch):
  • webcam demo:
  • notes:
  • notes:
  • github("Fast R-CNN in MXNet"):
  • github:
  • github:
  • github:

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

  • intro: CVPR 2017
  • arxiv:
  • paper:
  • github(Caffe):

Faster R-CNN

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

  • intro: NIPS 2015
  • arxiv:
  • gitxiv:
  • slides:
  • github(official, Matlab):
  • github(Caffe):
  • github(MXNet):
  • github(PyTorch--recommend):
  • github:
  • github(Torch)::
  • github(Torch)::
  • github(TensorFlow):
  • github(TensorFlow):
  • github(C++ demo):
  • github(Keras):
  • github:
  • github(C++):

R-CNN minus R

  • intro: BMVC 2015
  • arxiv:

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

  • github:

Contextual Priming and Feedback for Faster R-CNN

  • intro: ECCV 2016. Carnegie Mellon University
  • paper:
  • poster:

An Implementation of Faster RCNN with Study for Region Sampling

Interpretable R-CNN

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

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

  • intro: CVPR 2018. ETH Zurich & ESAT/PSI
  • arxiv:

Mask 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

  • arxiv:
  • github:

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

  • intro: ECCV 2014 / TPAMI 2015
  • arxiv:
  • github:
  • notes:

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

  • intro: PAMI 2016
  • intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
  • project page:
  • arxiv:

Object Detectors Emerge in Deep Scene CNNs

  • intro: ICLR 2015
  • arxiv:
  • paper:
  • paper:
  • slides:

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

  • intro: CVPR 2015
  • project(code+data):
  • arxiv:
  • github:

Object Detection Networks on Convolutional Feature Maps

  • intro: TPAMI 2015
  • keywords: NoC
  • arxiv:

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

  • arxiv:
  • slides:
  • github:

DeepBox: Learning Objectness with Convolutional Networks

  • keywords: DeepBox
  • arxiv:
  • github:

YOLO

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

img

  • arxiv:
  • code:
  • github:
  • blog:
  • slides:
  • reddit:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:

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

  • blog:
  • github:

Start Training YOLO with Our Own Data

img

  • intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
  • blog:
  • github:

YOLO: Core ML versus MPSNNGraph

  • intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
  • blog:
  • github:

TensorFlow YOLO object detection on Android

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

Computer Vision in iOS – Object Detection

  • blog:
  • github:

YOLOv2

YOLO9000: Better, Faster, Stronger

  • arxiv:
  • code: https://pjreddie.com/darknet/yolov2/
  • github(Chainer):
  • github(Keras):
  • github(PyTorch):
  • github(Tensorflow):
  • github(Windows):
  • github:
  • github:
  • github(TensorFlow):
  • github(Keras):
  • github(Keras):
  • github(TensorFlow):

darknet_scripts

  • intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
  • github:

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

  • github:

LightNet: Bringing pjreddie's DarkNet out of the shadows

YOLO v2 Bounding Box Tool

  • intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
  • github:

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

  • intro: LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
  • arxiv: https://arxiv.org/abs/1804.04606

Object detection at 200 Frames Per Second

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

OmniDetector: With Neural Networks to Bounding Boxes

YOLOv3

YOLOv3: An Incremental Improvement

YOLT

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

SSD

SSD: Single Shot MultiBox Detector

img

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

DSSD

DSSD : Deconvolutional Single Shot Detector

  • intro: UNC Chapel Hill & Amazon Inc
  • arxiv:
  • github:
  • github:
  • demo:

Enhancement of SSD by concatenating feature maps for object detection

  • intro: rainbow SSD (R-SSD)
  • arxiv:

Context-aware Single-Shot Detector

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

Feature-Fused SSD: Fast Detection for Small Objects

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

Weaving Multi-scale Context for Single Shot Detector

  • intro: WeaveNet
  • keywords: fuse multi-scale information
  • arxiv:

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

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

Pelee

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

https://github.com/Robert-JunWang/Pelee

Fire SSD

Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

R-FCN

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

  • arxiv:
  • github:
  • github(MXNet):
  • github:
  • github:
  • github:
  • github:

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

Recycle deep features for better object detection

  • arxiv:

FPN

Feature Pyramid Networks for Object Detection

  • intro: Facebook AI Research
  • arxiv:

Action-Driven Object Detection with Top-Down Visual Attentions

  • arxiv:

Beyond Skip Connections: Top-Down Modulation for Object Detection

  • intro: CMU & UC Berkeley & Google Research
  • arxiv:

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

  • intro: Inha University
  • arxiv:

Attentional Network for Visual Object Detection

  • intro: University of Maryland & Mitsubishi Electric Research Laboratories
  • arxiv:

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
  • arxiv:

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

  • intro: ICCV 2017 (poster)
  • arxiv:

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

  • intro: CVPR 2017
  • arxiv:

Spatial Memory for Context Reasoning in Object Detection

  • arxiv:

Accurate Single Stage Detector Using Recurrent Rolling Convolution

  • intro: CVPR 2017. SenseTime
  • keywords: Recurrent Rolling Convolution (RRC)
  • arxiv:
  • github:

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
  • arxiv:

Point Linking Network for Object Detection

  • intro: Point Linking Network (PLN)
  • arxiv:

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

  • intro: CVPR 2017
  • arxiv:
  • github:

Mimicking Very Efficient Network for Object Detection

  • intro: CVPR 2017. SenseTime & Beihang University
  • paper:

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
  • arxiv:

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

  • intro: ICCV 2017
  • arxiv:

Recurrent Scale Approximation for Object Detection in CNN

  • intro: ICCV 2017
  • keywords: Recurrent Scale Approximation (RSA)
  • arxiv:
  • github:

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

img

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

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

Object Detection from Scratch with Deep Supervision

RetinaNet

Focal Loss for Dense Object Detection

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

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

  • intro: ICCV 2017
  • arxiv:

Incremental Learning of Object Detectors without Catastrophic Forgetting

  • intro: ICCV 2017. Inria
  • arxiv:

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
  • arxiv:

MegDet

MegDet: A Large Mini-Batch Object Detector

  • intro: Peking University & Tsinghua University & Megvii Inc
  • arxiv:

Receptive Field Block Net for Accurate and Fast Object Detection

  • intro: RFBNet
  • arxiv:
  • github:

An Analysis of Scale Invariance in Object Detection - SNIP

  • arxiv:
  • github:

Feature Selective Networks for Object Detection

Learning a Rotation Invariant Detector with Rotatable Bounding Box

  • arxiv:
  • github:

Scalable Object Detection for Stylized Objects

  • intro: Microsoft AI & Research Munich
  • arxiv:

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

  • arxiv:
  • github:

Deep Regionlets for Object Detection

  • keywords: region selection network, gating network
  • arxiv:

Training and Testing Object Detectors with Virtual Images

  • intro: IEEE/CAA Journal of Automatica Sinica
  • arxiv:

Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video

  • keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
  • arxiv:

Spot the Difference by Object Detection

  • intro: Tsinghua University & JD Group
  • arxiv:

Localization-Aware Active Learning for Object Detection

  • arxiv:

Object Detection with Mask-based Feature Encoding

  • arxiv:

LSTD: A Low-Shot Transfer Detector for Object Detection

  • intro: AAAI 2018
  • arxiv:

Pseudo Mask Augmented Object Detection

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Learning Region Features for Object Detection

  • intro: Peking University & MSRA
  • arxiv:

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

  • intro: Singapore Management University & Zhejiang University
  • arxiv:

Object Detection for Comics using Manga109 Annotations

  • intro: University of Tokyo & National Institute of Informatics, Japan
  • arxiv:

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

  • arxiv:

Transferring Common-Sense Knowledge for Object Detection

  • arxiv:

Multi-scale Location-aware Kernel Representation for Object Detection

  • intro: CVPR 2018
  • arxiv:
  • github:

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

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

RefineNet

Single-Shot Refinement Neural Network for Object Detection

DetNet

DetNet: A Backbone network for Object Detection

SSOD

Self-supervisory Signals for Object Discovery and Detection

CornerNet

CornerNet: Detecting Objects as Paired Keypoints

M2Det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

3D Object Detection

3D Backbone Network for 3D Object Detection

LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs

ZSD（Zero-Shot Object Detection）

Zero-Shot Detection

  • intro: Australian National University
  • keywords: YOLO
  • arxiv:

Zero-Shot Object Detection

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

Zero-Shot Object Detection by Hybrid Region Embedding

OSD（One-Shot Object Detection）

Comparison Network for One-Shot Conditional Object Detection

One-Shot Object Detection

RepMet: Representative-based metric learning for classification and one-shot object detection

Weakly Supervised Object Detection

Weakly Supervised Object Detection in Artworks

Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

Softer-NMS

《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》

2019

Feature Selective Anchor-Free Module for Single-Shot Object Detection

Object Detection based on Region Decomposition and Assembly

Bottom-up Object Detection by Grouping Extreme and Center Points

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

Consistent Optimization for Single-Shot Object Detection

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

Region Proposal by Guided Anchoring

Scale-Aware Trident Networks for Object Detection

2018

Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions

Strong-Weak Distribution Alignment for Adaptive Object Detection

AutoFocus: Efficient Multi-Scale Inference

  • intro: AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU
  • arXiv: https://arxiv.org/abs/1812.01600

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

Grid R-CNN

Deformable ConvNets v2: More Deformable, Better Results

Anchor Box Optimization for Object Detection

Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection

Gradient Harmonized Single-stage Detector

CFENet: Object Detection with Comprehensive Feature Enhancement Module

DeRPN: Taking a further step toward more general object detection

Hybrid Knowledge Routed Modules for Large-scale Object Detection

《Receptive Field Block Net for Accurate and Fast Object Detection》

Deep Feature Pyramid Reconfiguration for Object Detection

Unsupervised Hard Example Mining from Videos for Improved Object Detection

Acquisition of Localization Confidence for Accurate Object Detection

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

MetaAnchor: Learning to Detect Objects with Customized Anchors

Relation Network for Object Detection

Quantization Mimic: Towards Very Tiny CNN for Object Detection

Learning Rich Features for Image Manipulation Detection

SNIPER: Efficient Multi-Scale Training

Soft Sampling for Robust Object Detection

Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria

Other

R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos

Detection Toolbox

  • Detectron(FAIR): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.
  • Detectron2: Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
  • maskrcnn-benchmark(FAIR): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
  • mmdetection(SenseTime&CUHK): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.