October 30, 2020

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

repo name amusi/awesome-object-detection
repo link https://github.com/amusi/awesome-object-detection
size (curr.) 79 kB
stars (curr.) 5947
created 2018-04-06



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


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》

《Deep Learning for Generic Object Detection: A Survey》



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: https://arxiv.org/abs/1804.04606

Object detection at 200 Frames Per Second

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

  • intro: YOLE–Object Detection in Neuromorphic Cameras
  • arxiv:https://arxiv.org/abs/1805.07931

OmniDetector: With Neural Networks to Bounding Boxes

  • intro: a person detector on n fish-eye images of indoor scenes(NIPS 2018)
  • arxiv:https://arxiv.org/abs/1805.08503
  • datasets:https://gitlab.com/omnidetector/omnidetector


YOLOv3: An Incremental Improvement

  • arxiv:https://arxiv.org/abs/1804.02767
  • paper:https://pjreddie.com/media/files/papers/YOLOv3.pdf
  • code: https://pjreddie.com/darknet/yolo/
  • github(Official):https://github.com/pjreddie/darknet
  • github:https://github.com/mystic123/tensorflow-yolo-v3
  • github:https://github.com/experiencor/keras-yolo3
  • github:https://github.com/qqwweee/keras-yolo3
  • github:https://github.com/marvis/pytorch-yolo3
  • github:https://github.com/ayooshkathuria/pytorch-yolo-v3
  • github:https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch
  • github:https://github.com/eriklindernoren/PyTorch-YOLOv3
  • github:https://github.com/ultralytics/yolov3
  • github:https://github.com/BobLiu20/YOLOv3_PyTorch
  • github:https://github.com/andy-yun/pytorch-0.4-yolov3
  • github:https://github.com/DeNA/PyTorch_YOLOv3


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

  • intro: Small Object Detection

  • arxiv:https://arxiv.org/abs/1805.09512

  • github:https://github.com/avanetten/yolt


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


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


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:https://arxiv.org/abs/1806.05363


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:https://arxiv.org/abs/1712.00886
  • github:https://github.com/szq0214/GRP-DSOD

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

Object Detection from Scratch with Deep Supervision


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: https://arxiv.org/abs/1712.08832

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

Robust Physical Adversarial Attack on Faster R-CNN Object Detector


Single-Shot Refinement Neural Network for Object Detection


DetNet: A Backbone network for Object Detection


Self-supervisory Signals for Object Discovery and Detection

  • Google Brain
  • arxiv:https://arxiv.org/abs/1806.03370


CornerNet: Detecting Objects as Paired Keypoints


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

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

  • intro: IBM Research AI
  • arxiv:https://arxiv.org/abs/1806.04728
  • github: TODO

Weakly Supervised Object Detection

Weakly Supervised Object Detection in Artworks

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


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


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


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

  • arxiv:https://arxiv.org/abs/1805.09300
  • github:https://github.com/mahyarnajibi/SNIPER

Soft Sampling for Robust Object Detection

  • intro: the robustness of object detection under the presence of missing annotations
  • arxiv:https://arxiv.org/abs/1806.06986

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


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.
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