awesome-object-detection

by amusi

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/20...

5.9K Stars 1.8K Forks Last release: Not found 85 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

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

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: https://arxiv.org/abs/1809.03193

《Deep Learning for Generic Object Detection: A Survey》

  • intro: Submitted to IJCV 2018
  • arXiv: https://arxiv.org/abs/1809.02165

Papers&Codes

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

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

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

YOLO

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

img

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

img

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

https://github.com//explosion/lightnet

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

  • intro: faster than Tiny-Yolo-v2
  • arxiv: https://arxiv.org/abs/1805.06361

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

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/YOLOv3tutorialfromscratch
  • 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

YOLT

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

SSD: Single Shot MultiBox Detector

img

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

https://github.com/weiliu89/caffe/issues/327

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

https://arxiv.org/abs/1709.05054

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector

https://arxiv.org/abs/1712.00960

Weaving Multi-scale Context for Single Shot Detector

ESSD

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

https://arxiv.org/abs/1801.05918

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

https://arxiv.org/abs/1802.06488

MDSSD

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

  • arxiv: https://arxiv.org/abs/1805.07009

Pelee

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

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

  • intro: (ICLR 2018 workshop track)

  • arxiv: https://arxiv.org/abs/1804.06882

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

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

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

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

https://arxiv.org/abs/1712.01802

Recycle deep features for better object detection

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

https://arxiv.org/abs/1704.05775

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

https://arxiv.org/abs/1706.05274

Few-shot Object Detection

https://arxiv.org/abs/1706.08249

Yes-Net: An effective Detector Based on Global Information

https://arxiv.org/abs/1706.09180

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

https://arxiv.org/abs/1706.10217

Towards lightweight convolutional neural networks for object detection

https://arxiv.org/abs/1707.01395

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

https://arxiv.org/abs/1707.05031

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

img

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

  • intro: BMVC 2018
  • arXiv: https://arxiv.org/abs/1807.11013

Object Detection from Scratch with Deep Supervision

  • intro: This is an extended version of DSOD
  • arXiv: https://arxiv.org/abs/1809.09294

RetinaNet

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

https://arxiv.org/abs/1709.04347

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

https://arxiv.org/abs/1709.05788

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

https://arxiv.org/abs/1711.05187

Zero-Annotation Object Detection with Web Knowledge Transfer

MegDet

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

https://arxiv.org/abs/1711.08879

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

https://arxiv.org/abs/1803.05858

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://arxiv.org/abs/1803.06799

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

Robust Physical Adversarial Attack on Faster R-CNN Object Detector

  • arxiv: https://arxiv.org/abs/1804.05810

RefineNet

Single-Shot Refinement Neural Network for Object Detection

DetNet

DetNet: A Backbone network for Object Detection

  • intro: Tsinghua University & Face++
  • arxiv: https://arxiv.org/abs/1804.06215

SSOD

Self-supervisory Signals for Object Discovery and Detection

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

CornerNet

CornerNet: Detecting Objects as Paired Keypoints

M2Det

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

  • intro: AAAI 2019
  • arXiv: https://arxiv.org/abs/1811.04533
  • github: https://github.com/qijiezhao/M2Det

3D Object Detection

3D Backbone Network for 3D Object Detection

  • arXiv: https://arxiv.org/abs/1901.08373

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

  • arxiv: https://arxiv.org/abs/1805.04902
  • github: https://github.com/CPFL/Autoware/tree/feature/cnnlidardetection

ZSD(Zero-Shot Object Detection)

Zero-Shot Detection

Zero-Shot Object Detection

  • arxiv: https://arxiv.org/abs/1804.04340

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

  • arxiv: https://arxiv.org/abs/1803.06049

Zero-Shot Object Detection by Hybrid Region Embedding

  • arxiv: https://arxiv.org/abs/1805.06157

OSD(One-Shot Object Detection)

Comparison Network for One-Shot Conditional Object Detection

  • arXiv: https://arxiv.org/abs/1904.02317

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

  • intro: ECCV 2018 Workshop Computer Vision for Art Analysis
  • arXiv: https://arxiv.org/abs/1810.02569
  • Datasets: https://wsoda.telecom-paristech.fr/downloads/dataset/IconArt_v1.zip

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

  • intro: CVPR 2018
  • arXiv: https://arxiv.org/abs/1803.11365
  • homepage: https://naoto0804.github.io/crossdomaindetection/
  • paper: http://openaccess.thecvf.com/contentcvpr2018/html/InoueCross-DomainWeakly-SupervisedObjectCVPR2018paper.html
  • github: https://github.com/naoto0804/cross-domain-detection

Softer-NMS

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

  • intro: CMU & Face++
  • arXiv: https://arxiv.org/abs/1809.08545
  • github: https://github.com/yihui-he/softer-NMS

2019

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

  • intro: CVPR 2019

  • arXiv: https://arxiv.org/abs/1903.00621

Object Detection based on Region Decomposition and Assembly

  • intro: AAAI 2019

  • arXiv: https://arxiv.org/abs/1901.08225

Bottom-up Object Detection by Grouping Extreme and Center Points

  • intro: one stage 43.2% on COCO test-dev
  • arXiv: https://arxiv.org/abs/1901.08043
  • github: https://github.com/xingyizhou/ExtremeNet

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

  • intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

  • arXiv: https://arxiv.org/abs/1901.07925

Consistent Optimization for Single-Shot Object Detection

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

  • arXiv: https://arxiv.org/abs/1901.06563

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

  • arXiv: https://arxiv.org/abs/1901.03796

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

  • arXiv: https://arxiv.org/abs/1901.03353
  • github: https://github.com/chengyangfu/retinamask

Region Proposal by Guided Anchoring

  • intro: CUHK - SenseTime Joint Lab
  • arXiv: https://arxiv.org/abs/1901.03278

Scale-Aware Trident Networks for Object Detection

  • intro: mAP of 48.4 on the COCO dataset
  • arXiv: https://arxiv.org/abs/1901.01892

2018

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

  • arXiv: https://arxiv.org/abs/1812.11901

Strong-Weak Distribution Alignment for Adaptive Object Detection

  • arXiv: https://arxiv.org/abs/1812.04798

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

  • intro: Google Could
  • arXiv: https://arxiv.org/abs/1812.00124

SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection

  • intro: UC Berkeley
  • arXiv: https://arxiv.org/abs/1812.00929

Grid R-CNN

  • intro: SenseTime
  • arXiv: https://arxiv.org/abs/1811.12030

Deformable ConvNets v2: More Deformable, Better Results

  • intro: Microsoft Research Asia

  • arXiv: https://arxiv.org/abs/1811.11168

Anchor Box Optimization for Object Detection

  • intro: Microsoft Research
  • arXiv: https://arxiv.org/abs/1812.00469

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

  • intro: https://arxiv.org/abs/1811.12152

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

  • arXiv: https://arxiv.org/abs/1812.00124

Learning RoI Transformer for Detecting Oriented Objects in Aerial Images

  • arXiv: https://arxiv.org/abs/1812.00155

Integrated Object Detection and Tracking with Tracklet-Conditioned Detection

  • intro: Microsoft Research Asia
  • arXiv: https://arxiv.org/abs/1811.11167

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

  • arXiv: https://arxiv.org/abs/1811.11318

Gradient Harmonized Single-stage Detector

  • intro: AAAI 2019
  • arXiv: https://arxiv.org/abs/1811.05181

CFENet: Object Detection with Comprehensive Feature Enhancement Module

  • intro: ACCV 2018
  • github: https://github.com/qijiezhao/CFENet

DeRPN: Taking a further step toward more general object detection

  • intro: AAAI 2019
  • arXiv: https://arxiv.org/abs/1811.06700
  • github: https://github.com/HCIILAB/DeRPN

Hybrid Knowledge Routed Modules for Large-scale Object Detection

  • intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab
  • arXiv: https://arxiv.org/abs/1810.12681
  • github: https://github.com/chanyn/HKRM

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

Deep Feature Pyramid Reconfiguration for Object Detection

  • intro: ECCV 2018
  • arXiv: https://arxiv.org/abs/1808.07993

Unsupervised Hard Example Mining from Videos for Improved Object Detection

  • intro: ECCV 2018
  • arXiv: https://arxiv.org/abs/1808.04285

Acquisition of Localization Confidence for Accurate Object Detection

  • intro: ECCV 2018
  • arXiv: https://arxiv.org/abs/1807.11590
  • github: https://github.com/vacancy/PreciseRoIPooling

Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

  • intro: ECCV 2018
  • arXiv: https://arxiv.org/abs/1807.09528

MetaAnchor: Learning to Detect Objects with Customized Anchors

  • arxiv: https://arxiv.org/abs/1807.00980

Relation Network for Object Detection

  • intro: CVPR 2018
  • arxiv: https://arxiv.org/abs/1711.11575
  • github:https://github.com/msracver/Relation-Networks-for-Object-Detection

Quantization Mimic: Towards Very Tiny CNN for Object Detection

  • Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3
  • arxiv: https://arxiv.org/abs/1805.02152

Learning Rich Features for Image Manipulation Detection

  • intro: CVPR 2018 Camera Ready
  • arxiv: https://arxiv.org/abs/1805.04953

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

  • intro: TNNLS 2018
  • arxiv:https://arxiv.org/abs/1807.00147
  • code: http://kezewang.com/codes/ASM_ver1.zip

Other

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

  • arxiv: https://arxiv.org/abs/1808.05560
  • youtube: https://youtu.be/xCYD-tYudN0

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.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.