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    README.md

    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》

    《Deep Learning for Generic Object Detection: A Survey》

    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

    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

    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

    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

    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

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

    Object Detection from Scratch with Deep Supervision

    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

    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

    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.

    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/amusi/awesome-object-detection

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