51 个深度学习目标检测模型汇总,论文、源码一应俱全!

目标检测(Object Detection)是深度学习 CV 领域的一个核心研究领域和重要分支。纵观 2013 年到 2019 年,从最早的 R-CNN、Fast R-CNN 到后来的 YOLO v2、YOLO v3 再到今年的 M2Det,新模型层出不穷,性能也越来越好!本文将会对目标检测近几年的发展和相关论文做出一份系统介绍,总结一份超全的文献 paper 列表。

模型列表先一睹为快!(建议收藏)

这份目标检测超全的技术路线总结来自于 GitHub 上一个知名项目,作者是 Lee hoseong,项目地址是:

https://github.com/hoya012/deep_learning_object_detection

该技术路线横跨时间是 2014 年至 2019 年,上图总结了这期间目标检测所有重要的模型。图中标红的部分是作者认为比较重要,需要重点掌握的模型。当然每个人有都有各自的评价。

模型性能比较

FPS(速度)索引与硬件规格(如 CPU、GPU、RAM 等)有关,因此很难进行同等比较。解决方案是在具有相同规格的硬件上测量所有模型的性能,但这是非常困难和耗时的。比较结果如下:

下面举例对标红的重要模型进行介绍!

2014 年

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation | Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik | [CVPR’ 14]

论文:

https://arxiv.org/pdf/1311.2524.pdf

代码 Caffe:

https://github.com/rbgirshick/rcnn

OverFeat

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | Pierre Sermanet, et al. | [ICLR’ 14]

论文:

https://arxiv.org/pdf/1312.6229.pdf

代码 Torch:

https://github.com/sermanet/OverFeat

2015 年

Fast R-CNN

Fast R-CNN | Ross Girshick | [ICCV’ 15]

论文:

https://arxiv.org/pdf/1504.08083.pdf

代码 caffe:

https://github.com/rbgirshick/fast-rcnn

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | Shaoqing Ren, et al. | [NIPS’ 15]

论文:

https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf

代码 caffe:

https://github.com/rbgirshick/py-faster-rcnn

代码 tensorflow:

https://github.com/endernewton/tf-faster-rcnn

代码 pytorch:

https://github.com/jwyang/faster-rcnn.pytorch

2016 年

OHEM

Training Region-based Object Detectors with Online Hard Example Mining | Abhinav Shrivastava, et al. | [CVPR’ 16]

论文:

https://arxiv.org/pdf/1604.03540.pdf

代码 caffe:

https://github.com/abhi2610/ohem

YOLO v1

You Only Look Once: Unified, Real-Time Object Detection | Joseph Redmon, et al. | [CVPR’ 16]

论文:

https://arxiv.org/pdf/1506.02640.pdf

代码 c:

https://pjreddie.com/darknet/yolo/

SSD

Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV’ 16]

论文:

https://arxiv.org/pdf/1512.02325.pdf

代码 caffe:

https://github.com/weiliu89/caffe/tree/ssd

代码 tensorflow:

https://github.com/balancap/SSD-Tensorflow

代码 pytorch:

https://github.com/amdegroot/ssd.pytorch

R-FCN

Object Detection via Region-based Fully Convolutional Networks | Jifeng Dai, et al. | [NIPS’ 16]

论文:

https://arxiv.org/pdf/1605.06409.pdf

代码 caffe:

https://github.com/daijifeng001/R-FCN

代码 caffe:

https://github.com/YuwenXiong/py-R-FCN

2017 年

YOLO v2

Better, Faster, Stronger | Joseph Redmon, Ali Farhadi | [CVPR’ 17]

论文:

https://arxiv.org/pdf/1612.08242.pdf

代码 c:

https://pjreddie.com/darknet/yolo/

代码 caffe:

https://github.com/quhezheng/caffe_yolo_v2

代码 tensorflow:

https://github.com/nilboy/tensorflow-yolo

代码 tensorflow:

https://github.com/sualab/object-detection-yolov2

代码 pytorch:

https://github.com/longcw/yolo2-pytorch

FPN

Feature Pyramid Networks for Object Detection | Tsung-Yi Lin, et al. | [CVPR’ 17]

论文:

http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.pdf

代码 caffe:

https://github.com/unsky/FPN

RetinaNet

Focal Loss for Dense Object Detection | Tsung-Yi Lin, et al. | [ICCV’ 17]

论文:

https://arxiv.org/pdf/1708.02002.pdf

代码 keras:

https://github.com/fizyr/keras-retinanet

代码 pytorch:

https://github.com/kuangliu/pytorch-retinanet

代码 mxnet:

https://github.com/unsky/RetinaNet

代码 tensorflow:

https://github.com/tensorflow/tpu/tree/master/models/official/retinanet

Mask R-CNN

Kaiming He, et al. | [ICCV’ 17]

论文:

http://openaccess.thecvf.com/content_ICCV_2017/papers/He_Mask_R-CNN_ICCV_2017_paper.pdf

代码 caffe2:

https://github.com/facebookresearch/Detectron

代码 tensorflow:

https://github.com/matterport/Mask_RCNN

代码 tensorflow:

https://github.com/CharlesShang/FastMaskRCNN

代码 pytorch:

https://github.com/multimodallearning/pytorch-mask-rcnn

2018 年

YOLO v3

An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv’ 18]

论文:

https://pjreddie.com/media/files/papers/YOLOv3.pdf

代码 c:

https://pjreddie.com/darknet/yolo/

代码 pytorch:

https://github.com/ayooshkathuria/pytorch-yolo-v3

代码 pytorch:

https://github.com/eriklindernoren/PyTorch-YOLOv3

代码 keras:

https://github.com/qqwweee/keras-yolo3

代码 tensorflow:

https://github.com/mystic123/tensorflow-yolo-v3

RefineDet

Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR’ 18]

论文:

http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single-Shot_Refinement_Neural_CVPR_2018_paper.pdf

代码 caffe:

https://github.com/sfzhang15/RefineDet

代码 chainer:

https://github.com/fukatani/RefineDet_chainer

代码 pytorch:

https://github.com/lzx1413/PytorchSSD

2019 年

M2Det

A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | Qijie Zhao, et al. | [AAAI’ 19]

论文:

https://arxiv.org/pdf/1811.04533.pdf

参考文献

该项目的参考文献来自于论文《Deep Learning for Generic Object Detection: A Survey》

论文地址:

https://arxiv.org/pdf/1809.02165v1.pdf


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