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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
  • 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:


Imbalance Problems in Object Detection: A Review

Recent Advances in Deep Learning for Object Detection

A Survey of Deep Learning-based Object Detection

Object Detection in 20 Years: A Survey

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

  • intro: awesome

  • arXiv:

《Deep Learning for Generic Object Detection: A Survey》

  • intro: Submitted to IJCV 2018
  • arXiv:



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

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

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


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


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

Computer Vision in iOS – Object Detection


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

YOLO v2 Bounding Box Tool

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:

Object detection at 200 Frames Per Second

  • intro: faster than Tiny-Yolo-v2
  • arxiv:

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

  • intro: YOLE--Object Detection in Neuromorphic Cameras
  • arxiv:

OmniDetector: With Neural Networks to Bounding Boxes

  • intro: a person detector on n fish-eye images of indoor scenes(NIPS 2018)
  • arxiv:
  • datasets:


YOLOv3: An Incremental Improvement

  • arxiv:
  • paper:
  • code:
  • github(Official):
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:
  • github:


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

  • intro: Small Object Detection

  • arxiv:

  • github:


SSD: Single Shot MultiBox Detector


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


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


FSSD: Feature Fusion Single Shot Multibox Detector

Weaving Multi-scale Context for Single Shot Detector


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

  • arxiv:


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

  • intro: (ICLR 2018 workshop track)

  • arxiv:

  • github:

Fire SSD

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

  • intro:low cost, fast speed and high mAP on factor edge computing devices

  • arxiv:


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


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

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: Learning Deeply Supervised Object Detectors from Scratch


Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

  • arxiv:
  • github:

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

  • intro: BMVC 2018
  • arXiv:

Object Detection from Scratch with Deep Supervision

  • intro: This is an extended version of DSOD
  • arXiv:


Focal Loss for Dense Object Detection

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

Zero-Annotation Object Detection with Web Knowledge Transfer


MegDet: A Large Mini-Batch Object Detector

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

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

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

Pseudo Mask Augmented Object Detection

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

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

  • intro: National University of Defense Technology
  • arxiv:

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

  • arxiv:


Single-Shot Refinement Neural Network for Object Detection


DetNet: A Backbone network for Object Detection

  • intro: Tsinghua University & Face++
  • arxiv:


Self-supervisory Signals for Object Discovery and Detection

  • Google Brain
  • arxiv:


CornerNet: Detecting Objects as Paired Keypoints


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

  • intro: AAAI 2019
  • arXiv:
  • github:

3D Object Detection

3D Backbone Network for 3D Object Detection

  • arXiv:

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

  • arxiv:
  • github:

ZSD(Zero-Shot Object Detection)

Zero-Shot Detection

Zero-Shot Object Detection

  • arxiv:

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

  • arxiv:

Zero-Shot Object Detection by Hybrid Region Embedding

  • arxiv:

OSD(One-Shot Object Detection)

Comparison Network for One-Shot Conditional Object Detection

  • arXiv:

One-Shot Object Detection

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

  • intro: IBM Research AI
  • arxiv:
  • github: TODO

Weakly Supervised Object Detection

Weakly Supervised Object Detection in Artworks

  • intro: ECCV 2018 Workshop Computer Vision for Art Analysis
  • arXiv:
  • Datasets:

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

  • intro: CVPR 2018
  • arXiv:
  • homepage:
  • paper:
  • github:


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

  • intro: CMU & Face++
  • arXiv:
  • github:


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

  • intro: CVPR 2019

  • arXiv:

Object Detection based on Region Decomposition and Assembly

  • intro: AAAI 2019

  • arXiv:

Bottom-up Object Detection by Grouping Extreme and Center Points

  • intro: one stage 43.2% on COCO test-dev
  • arXiv:
  • github:

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


  • arXiv:

Consistent Optimization for Single-Shot Object Detection

  • intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase

  • arXiv:

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

  • arXiv:

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

  • arXiv:
  • github:

Region Proposal by Guided Anchoring

  • intro: CUHK - SenseTime Joint Lab
  • arXiv:

Scale-Aware Trident Networks for Object Detection

  • intro: mAP of 48.4 on the COCO dataset
  • arXiv:


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

  • arXiv:

Strong-Weak Distribution Alignment for Adaptive Object Detection

  • arXiv:

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:

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

  • intro: Google Could
  • arXiv:

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

  • intro: UC Berkeley
  • arXiv:

Grid R-CNN

  • intro: SenseTime
  • arXiv:

Deformable ConvNets v2: More Deformable, Better Results

  • intro: Microsoft Research Asia

  • arXiv:

Anchor Box Optimization for Object Detection

  • intro: Microsoft Research
  • arXiv:

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

  • intro:

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

  • arXiv:

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

  • arXiv:

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

  • intro: Microsoft Research Asia
  • arXiv:

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

  • arXiv:

Gradient Harmonized Single-stage Detector

  • intro: AAAI 2019
  • arXiv:

CFENet: Object Detection with Comprehensive Feature Enhancement Module

  • intro: ACCV 2018
  • github:

DeRPN: Taking a further step toward more general object detection

  • intro: AAAI 2019
  • arXiv:
  • github:

Hybrid Knowledge Routed Modules for Large-scale Object Detection

  • intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab
  • arXiv:
  • github:

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

Deep Feature Pyramid Reconfiguration for Object Detection

  • intro: ECCV 2018
  • arXiv:

Unsupervised Hard Example Mining from Videos for Improved Object Detection

  • intro: ECCV 2018
  • arXiv:

Acquisition of Localization Confidence for Accurate Object Detection

  • intro: ECCV 2018
  • arXiv:
  • github:

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

  • intro: ECCV 2018
  • arXiv:

MetaAnchor: Learning to Detect Objects with Customized Anchors

  • arxiv:

Relation Network for Object Detection

  • intro: CVPR 2018
  • arxiv:
  • github:

Quantization Mimic: Towards Very Tiny CNN for Object Detection

  • Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3
  • arxiv:

Learning Rich Features for Image Manipulation Detection

  • intro: CVPR 2018 Camera Ready
  • arxiv:

SNIPER: Efficient Multi-Scale Training

  • arxiv:
  • github:

Soft Sampling for Robust Object Detection

  • intro: the robustness of object detection under the presence of missing annotations
  • arxiv:

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

  • intro: TNNLS 2018
  • arxiv:
  • code:


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

  • arxiv:
  • youtube:

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.

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