深度学习物体检测论文阅读路线图以及官方实现代码

点击上方“AI公园”，关注公众号，选择加“星标“或“置顶”

作者：hoya012

编译：ronghuaiyang

物体检测是CV领域非常重要的场景，自从2012年深度学习开始发威以来，物体检测也不例外的屈服于深度学习的淫威之下，特别是RCNN以来，物体检测进展飞速，各种网络，各种loss，各种trick，层出不穷，perfermance也是一路飙升，今天在github上找到一个repo，整理了2014到目前为止的物体检测的论文列表，还有对应的官方代码哦。好了，废话少说，让我们进入正题。

github地址：https://github.com/hoya012/deep_learning_object_detection

使用深度学习的物体检测论文列表，参考了这篇文章：https://arxiv.org/pdf/1809.02165v1.pdf。

最近更新: 2018/12/07

从2014年到现在（2018年）的论文列表

performance 表

2014年的论文

• [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik | [CVPR' 14] |[[pdf\]](https://arxiv.org/pdf/1311.2524.pdf) [[official code - caffe\]](https://github.com/rbgirshick/rcnn)

• [OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | Pierre Sermanet, et al. | [ICLR' 14] |[[pdf\]](https://arxiv.org/pdf/1312.6229.pdf) [[official code - torch\]](https://github.com/sermanet/OverFeat)

• [MultiBox] Scalable Object Detection using Deep Neural Networks | Dumitru Erhan, et al. | [CVPR' 14] |[[pdf\]](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf)

• [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | Kaiming He, et al. | [ECCV' 14]|[[pdf\]](https://arxiv.org/pdf/1406.4729.pdf) [[official code - caffe\]](https://github.com/ShaoqingRen/SPP_net) [[unofficial code - keras\]](https://github.com/yhenon/keras-spp) [[unofficial code - tensorflow\]](https://github.com/peace195/sppnet)

2015的论文

• [MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | Spyros Gidaris, Nikos Komodakis | [ICCV' 15] |[[pdf\]](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Gidaris_Object_Detection_via_ICCV_2015_paper.pdf) [[official code - caffe\]](https://github.com/gidariss/mrcnn-object-detection)

• [DeepBox] DeepBox: Learning Objectness with Convolutional Networks | Weicheng Kuo, Bharath Hariharan, Jitendra Malik | [ICCV' 15] |[[pdf\]](https://arxiv.org/pdf/1505.02146.pdf) [[official code - caffe\]](https://github.com/weichengkuo/DeepBox)

• [AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | Donggeun Yoo, et al. | [ICCV' 15] |[[pdf\]](https://arxiv.org/pdf/1506.07704.pdf)

• [Fast R-CNN] Fast R-CNN | Ross Girshick | [ICCV' 15] |[[pdf\]](https://arxiv.org/pdf/1504.08083.pdf) [[official code - caffe\]](https://github.com/rbgirshick/fast-rcnn)

• [DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | Amir Ghodrati, et al. | [ICCV' 15] |[[pdf\]](https://arxiv.org/pdf/1510.04445.pdf) [[official code - matconvnet\]](https://github.com/aghodrati/deepproposal)

• [Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | Shaoqing Ren, et al. | [NIPS' 15] |[[pdf\]](https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf) [[official code - caffe\]](https://github.com/rbgirshick/py-faster-rcnn) [[unofficial code - tensorflow\]](https://github.com/endernewton/tf-faster-rcnn) [[unofficial code - pytorch\]](https://github.com/jwyang/faster-rcnn.pytorch)

2016的论文

• [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | Joseph Redmon, et al. | [CVPR' 16] |[[pdf\]](https://arxiv.org/pdf/1506.02640.pdf)[[official code - c\]](https://pjreddie.com/darknet/yolo/)

• [G-CNN] G-CNN: an Iterative Grid Based Object Detector | Mahyar Najibi, et al. | [CVPR' 16] |[[pdf\]](https://arxiv.org/pdf/1512.07729.pdf)

• [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | Yongxi Lu, Tara Javidi. | [CVPR' 16] |[[pdf\]](https://arxiv.org/pdf/1512.07711.pdf)

• [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | Sean Bell, et al. | [CVPR' 16] |[[pdf\]](https://arxiv.org/pdf/1512.04143.pdf)

• [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | Tao Kong, et al. | [CVPR' 16] |[[pdf\]](https://arxiv.org/pdf/1604.00600.pdf)

• [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | Abhinav Shrivastava, et al. | [CVPR' 16] |[[pdf\]](https://arxiv.org/pdf/1604.03540.pdf) [[official code - caffe\]](https://github.com/abhi2610/ohem)

• [CRAPF] CRAFT Objects from Images | Bin Yang, et al. | [CVPR' 16] |[[pdf\]](https://arxiv.org/pdf/1604.03239.pdf) [[official code - caffe\]](https://github.com/byangderek/CRAFT)

• [MPN] A MultiPath Network for Object Detection | Sergey Zagoruyko, et al. | [BMVC' 16] |[[pdf\]](https://arxiv.org/pdf/1604.02135.pdf) [[official code - torch\]](https://github.com/facebookresearch/multipathnet)

• [SSD] SSD: Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV' 16] |[[pdf\]](https://arxiv.org/pdf/1512.02325.pdf) [[official code - caffe\]](https://github.com/weiliu89/caffe/tree/ssd) [[unofficial code - tensorflow\]](https://github.com/balancap/SSD-Tensorflow) [[unofficial code - pytorch\]](https://github.com/amdegroot/ssd.pytorch)

• [GBDNet] Crafting GBD-Net for Object Detection | Xingyu Zeng, et al. | [ECCV' 16] |[[pdf\]](https://arxiv.org/pdf/1610.02579.pdf) [[official code - caffe\]](https://github.com/craftGBD/craftGBD)

• [CPF] Contextual Priming and Feedback for Faster R-CNN | Abhinav Shrivastava and Abhinav Gupta | [ECCV' 16] |[[pdf\]](https://pdfs.semanticscholar.org/40e7/4473cb82231559cbaeaa44989e9bbfe7ec3f.pdf)

• [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | Zhaowei Cai, et al. | [ECCV' 16] |[[pdf\]](https://arxiv.org/pdf/1607.07155.pdf) [[official code - caffe\]](https://github.com/zhaoweicai/mscnn)

• [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | Jifeng Dai, et al. | [NIPS' 16] |[[pdf\]](https://arxiv.org/pdf/1605.06409.pdf)[[official code - caffe\]](https://github.com/daijifeng001/R-FCN) [[unofficial code - caffe\]](https://github.com/YuwenXiong/py-R-FCN)

• [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | Kye-Hyeon Kim, et al. | [NIPSW' 16] |[[pdf\]](https://arxiv.org/pdf/1608.08021.pdf) [[official code - caffe\]](https://github.com/sanghoon/pva-faster-rcnn)

• [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | Wanli Ouyang, et al. | [PAMI' 16] |[[pdf\]](https://arxiv.org/pdf/1412.5661.pdf)

• [NoC] Object Detection Networks on Convolutional Feature Maps | Shaoqing Ren, et al. | [TPAMI' 16] |[[pdf\]](https://arxiv.org/pdf/1504.06066.pdf)

2017的论文

• [DSSD] DSSD : Deconvolutional Single Shot Detector | Cheng-Yang Fu1, et al. | [arXiv' 17] |[[pdf\]](https://arxiv.org/pdf/1701.06659.pdf) [[official code - caffe\]](https://github.com/chengyangfu/caffe/tree/dssd)

• [TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | Abhinav Shrivastava, et al. | [CVPR' 17] |[[pdf\]](https://arxiv.org/pdf/1612.06851.pdf)

• [FPN] Feature Pyramid Networks for Object Detection | Tsung-Yi Lin, et al. | [CVPR' 17] |[[pdf\]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.pdf) [[unofficial code - caffe\]](https://github.com/unsky/FPN)

• [YOLO v2] YOLO9000: Better, Faster, Stronger | Joseph Redmon, Ali Farhadi | [CVPR' 17] |[[pdf\]](https://arxiv.org/pdf/1612.08242.pdf) [[official code - c\]](https://pjreddie.com/darknet/yolo/)[[unofficial code - caffe\]](https://github.com/quhezheng/caffe_yolo_v2) [[unofficial code - tensorflow\]](https://github.com/nilboy/tensorflow-yolo) [[unofficial code - tensorflow\]](https://github.com/sualab/object-detection-yolov2) [[unofficial code - pytorch\]](https://github.com/longcw/yolo2-pytorch)

• [RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | Tao Kong, et al. | [CVPR' 17] |[[pdf\]](https://arxiv.org/pdf/1707.01691.pdf) [[official code - caffe\]](https://github.com/taokong/RON) [[unofficial code - tensorflow\]](https://github.com/HiKapok/RON_Tensorflow)

• [DCN] Deformable Convolutional Networks | Jifeng Dai, et al. | [ICCV' 17] |[[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Dai_Deformable_Convolutional_Networks_ICCV_2017_paper.pdf) [[official code - mxnet\]](https://github.com/msracver/Deformable-ConvNets) [[unofficial code - tensorflow\]](https://github.com/Zardinality/TF_Deformable_Net) [[unofficial code - pytorch\]](https://github.com/oeway/pytorch-deform-conv)

• [DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | Lachlan Tychsen-Smith, Lars Petersson | [ICCV' 17] |[[pdf\]](https://arxiv.org/pdf/1703.10295.pdf) [[official code - theano\]](https://github.com/lachlants/denet)

• [CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | Yousong Zhu, et al. | [ICCV' 17]|[[pdf\]](https://arxiv.org/pdf/1708.02863.pdf) [[official code - caffe\]](https://github.com/tshizys/CoupleNet)

• [RetinaNet] Focal Loss for Dense Object Detection | Tsung-Yi Lin, et al. | [ICCV' 17] |[[pdf\]](https://arxiv.org/pdf/1708.02002.pdf) [[official code - keras\]](https://github.com/fizyr/keras-retinanet)[[unofficial code - pytorch\]](https://github.com/kuangliu/pytorch-retinanet) [[unofficial code - mxnet\]](https://github.com/unsky/RetinaNet) [[unofficial code - tensorflow\]](https://github.com/tensorflow/tpu/tree/master/models/official/retinanet)

• [Mask R-CNN] Mask R-CNN | Kaiming He, et al. | [ICCV' 17] |[[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/He_Mask_R-CNN_ICCV_2017_paper.pdf) [[official code - caffe2\]](https://github.com/facebookresearch/Detectron) [[unofficial code - tensorflow\]](https://github.com/matterport/Mask_RCNN) [[unofficial code - tensorflow\]](https://github.com/CharlesShang/FastMaskRCNN) [[unofficial code - pytorch\]](https://github.com/multimodallearning/pytorch-mask-rcnn)

• [DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | Zhiqiang Shen, et al. | [ICCV' 17] |[[pdf\]](https://arxiv.org/pdf/1708.01241.pdf)[[official code - caffe\]](https://github.com/szq0214/DSOD) [[unofficial code - pytorch\]](https://github.com/uoip/SSD-variants)

• [SMN] Spatial Memory for Context Reasoning in Object Detection | Xinlei Chen, Abhinav Gupta | [ICCV' 17] |[[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Spatial_Memory_for_ICCV_2017_paper.pdf)

2018的论文

• [YOLO v3] YOLOv3: An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv' 18] |[[pdf\]](https://pjreddie.com/media/files/papers/YOLOv3.pdf) [[official code - c\]](https://pjreddie.com/darknet/yolo/)[[unofficial code - pytorch\]](https://github.com/ayooshkathuria/pytorch-yolo-v3) [[unofficial code - pytorch\]](https://github.com/eriklindernoren/PyTorch-YOLOv3) [[unofficial code - keras\]](https://github.com/qqwweee/keras-yolo3) [[unofficial code - tensorflow\]](https://github.com/mystic123/tensorflow-yolo-v3)

• [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | Hongyang Li, et al. | [IJCV' 18] |[[pdf\]](https://arxiv.org/pdf/1702.05711.pdf)[[official code - caffe\]](https://github.com/hli2020/zoom_network)

• [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | Yong Liu, et al. | [CVPR' 18] |[[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Structure_Inference_Net_CVPR_2018_paper.pdf) [[official code - tensorflow\]](https://github.com/choasup/SIN)

• [STDN] Scale-Transferrable Object Detection | Peng Zhou, et al. | [CVPR' 18] |[[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Scale-Transferrable_Object_Detection_CVPR_2018_paper.pdf)

• [RefineDet] Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR' 18] |[[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single-Shot_Refinement_Neural_CVPR_2018_paper.pdf)[[official code - caffe\]](https://github.com/sfzhang15/RefineDet) [[unofficial code - chainer\]](https://github.com/fukatani/RefineDet_chainer) [[unofficial code - pytorch\]](https://github.com/lzx1413/PytorchSSD)

• [MegDet] MegDet: A Large Mini-Batch Object Detector | Chao Peng, et al. | [CVPR' 18] |[[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Peng_MegDet_A_Large_CVPR_2018_paper.pdf)

• [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | Yuhua Chen, et al. | [CVPR' 18] |[[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Domain_Adaptive_Faster_CVPR_2018_paper.pdf) [[official code - caffe\]](https://github.com/yuhuayc/da-faster-rcnn)

• [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | Bharat Singh, Larry S. Davis | [CVPR' 18] |[[pdf\]](https://arxiv.org/pdf/1711.08189.pdf)

• [Relation-Network] Relation Networks for Object Detection | Han Hu, et al. | [CVPR' 18] |[[pdf\]](https://arxiv.org/pdf/1711.11575.pdf) [[official code - mxnet\]](https://github.com/msracver/Relation-Networks-for-Object-Detection)

• [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | Zhaowei Cai, et al. | [CVPR' 18] |[[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cai_Cascade_R-CNN_Delving_CVPR_2018_paper.pdf)[[official code - caffe\]](https://github.com/zhaoweicai/cascade-rcnn)

• Finding Tiny Faces in the Wild with Generative Adversarial Network | Yancheng Bai, et al. | [CVPR' 18] |[[pdf\]](https://ivul.kaust.edu.sa/Documents/Publications/2018/Finding%20Tiny%20Faces%20in%20the%20Wild%20with%20Generative%20Adversarial%20Network.pdf)

• [STDnet] STDnet: A ConvNet for Small Target Detection | Brais Bosquet, et al. | [BMVC' 18] |[[pdf\]](http://bmvc2018.org/contents/papers/0897.pdf)

• [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | Songtao Liu, et al. | [ECCV' 18] |[[pdf\]](https://arxiv.org/pdf/1711.07767.pdf)[[official code - pytorch\]](https://github.com/ruinmessi/RFBNet)

• Zero-Annotation Object Detection with Web Knowledge Transfer | Qingyi Tao, et al. | [ECCV' 18] |[[pdf\]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Qingyi_Tao_Zero-Annotation_Object_Detection_ECCV_2018_paper.pdf)

• [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | Hei Law, et al. | [ECCV' 18] |[[pdf\]](https://arxiv.org/pdf/1808.01244.pdf) [[official code - pytorch\]](https://github.com/princeton-vl/CornerNet)

• [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | Jun Wang, et al. | [NIPS' 18] |[[pdf\]](http://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices.pdf) [[official code - caffe\]](https://github.com/Robert-JunWang/Pelee)

• [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | ChenHan Jiang, et al. | [NIPS' 18] |[[pdf\]](http://papers.nips.cc/paper/7428-hybrid-knowledge-routed-modules-for-large-scale-object-detection.pdf)

• [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | Tong Yang, et al. | [NIPS' 18] |[[pdf\]](http://papers.nips.cc/paper/7315-metaanchor-learning-to-detect-objects-with-customized-anchors.pdf)

• [SNIPER] SNIPER: Efficient Multi-Scale Training | Bharat Singh, et al. | [NIPS' 18] |[[pdf\]](http://papers.nips.cc/paper/8143-sniper-efficient-multi-scale-training.pdf)

2019的论文

• [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | Qijie Zhao, et al. | [AAAI' 19] |[[pdf\]](https://arxiv.org/pdf/1811.04533.pdf)

往期精彩回顾

1、最全的AI速查表|神经网络，机器学习，深度学习，大数据

2、资源|10个机器学习和深度学习的必读免费课程

3、论文看吐了没有？做研究的同学瞧一瞧看一看啦，教你读论文：为什么读以及如何读

4、人人都能看得懂的深度学习介绍！全篇没有一个数学符号！

5、想找个数据科学家的工作吗？别再随大流了！

本文可以任意转载，转载时请注明作者及原文地址。

请长按或扫描二维码关注本公众号