基于深度学习的YOLO目标检测综述

邵延华; 张铎; 楚红雨; 张晓强; 饶云波

doi:10.11999/JEIT210790

基于深度学习的YOLO目标检测综述

doi: 10.11999/JEIT210790

1.
西南科技大学信息工程学院绵阳 621010
2.
电子科技大学成都 610054

基金项目: 国家自然科学基金(61601382)，四川省科技计划(2019YJ0325, 2020YFG0148, 2021YFG0314)

详细信息

作者简介:
邵延华：男，讲师，研究方向为计算机视觉

张铎：男，硕士生，研究方向为计算机视觉

楚红雨：男，副研究员，研究方向为机器人技术

张晓强：男，讲师，研究方向为合成孔径成像和计算机视觉

饶云波：男，副教授，研究方向为虚拟现实、互联网和计算机视觉

通讯作者:
邵延华　syh@cqu.edu.cn

中图分类号: TN911.73
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- 被引次数: 0
出版历程
- 收稿日期: 2021-08-06
- 修回日期: 2022-01-22
- 录用日期: 2022-02-16
- 网络出版日期: 2022-02-19
- 刊出日期: 2022-10-19

A Review of YOLO Object Detection Based on Deep Learning

1.
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
2.
University of Electronic Science & Technology of China, Chengdu 610054, China

Funds: The National Natural Science Foundation of China (61601382), Sichuan Provincial Science and Technology Project (2019YJ0325, 2020YFG0148, 2021YFG0314)

摘要

摘要: 目标检测是计算机视觉领域的一个基础任务和研究热点。YOLO将目标检测概括为一个回归问题，实现端到端的训练和检测，由于其良好的速度-精度平衡，近几年一直处于目标检测领域的领先地位，被成功地研究、改进和应用到众多不同领域。该文对YOLO系列算法及其重要改进、应用进行了详细调研。首先，系统地梳理了YOLO家族及重要改进，包含YOLOv1-v4, YOLOv5, Scaled-YOLOv4, YOLOR和最新的YOLOX。然后，对YOLO中重要的基础网络，损失函数进行了详细的分析和总结。其次，依据不同的改进思路或应用场景对YOLO算法进行了系统的分类归纳。例如，注意力机制、3D、航拍场景、边缘计算等。最后，总结了YOLO的特点，并结合最新的文献分析可能的改进思路和研究趋势。
- 目标检测 /
- YOLO /
- 深度学习 /
- 卷积神经网络
Abstract: Object detection is one of the basic tasks and research hotspots in the field of computer vision. The YOLO (You Only Look Once) frames object detection is a regression problem to implement end-to-end training and detection. YOLO becomes the leading object detector due to its good speed-accuracy balance, which has been successfully studied, improved, and applied to many different fields. YOLO series and its important improvements and applications are investigated in detail. Firstly, the YOLO family and important improvements are systematically summarized, including YOLOv1-v4, YOLOv5, Scaled-YOLOv4, YOLOR, and the latest YOLOX. Then, important backbone and loss functions in YOLO are analyzed and summarized in detail. Next, the application of YOLO is systematically classified and summarized according to different improvement ideas or scenarios, such as attention mechanisms, three-dimensional scenes, aerial scenes, edge computing, etc. Finally, the characteristics of the YOLO series are summarized and the possible improvement ideas and research trends are analyzed in combination with the latest literature.
- Object detection /
- YOLO /
- Deep learning /
- Convolutional Neural Network (CNN)

HTML全文

图 1 YOLO检测模型的发展历程

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图 2 YOLOv1的网络结构

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图 3 具有尺寸先验和位置预测的边界框

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图 4 Darknet-53与CSPDarknet-53

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图 5 VisDrone2019数据集示例^[37]

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图 6 Kaggle小麦检测数据集与PRCV比赛数据集示例

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表 1 YOLO系列在VOC2012的检测结果

检测框架	mAP(%)	fps	GPU
YOLO^[8]	57.9	–	TitanX
YOLOv3 416^[12]	79.3	39	1080Ti
SPP-YOLO 416^[39]	77.5	65.2	1080Ti
DC-SPP-YOLO 416^[39]	78.4	56.3	1080Ti
GC-YOLOv3 544^[31]	83.7	31	1080Ti

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表 2 各类YOLO算法在COCO test2017上的表现

检测框架	主干网络	尺寸	fps	AP	AP₅₀	AP₇₅	AP_S	AP_M	AP_L	GPU
YOLOv3^[12], arXiv2018	Darknet-53	416	35	31.0	55.3	32.3	15.2	33.2	42.8	Maxwell GPU
YOLOv3-tiny^[12], arXiv2018	Darknet Ref	416	330	–	33.1	–	–	–	–	GTX 1080Ti
GC-YOLOv3^[31], MDPI2020	Darknet 53	416	28	–	55.5	–	–	–	–	GTX 1080Ti
YOLOv4-CSP^[13], arXiv2020	CSPDarknet-53	640	70	47.5	66.2	51.7	28.2	51.2	59.8	Volta GPU
YOLOv5-S^[14]	Modified CSP v5	640	156.3	36.7	55.4	–	–	–	–	Volta GPU
YOLOv5-X^[14]	Modified CSP v5	640	82.6	50.4	68.8	–	–	–	–	Volta GPU
PP-YOLOv2^[40], arXiv2021	ResNet50-vd-dcn^[28]	640	68.9	49.5	68.2	54.4	30.7	52.9	61.2	Volta GPU
YOLOR-P6^[9], arXiv2021	–	1280	49	52.6	70.6	57.6	34.7	56.6	64.2	Volta GPU
YOLOX-X^[10], arXiv2021	Modified CSP v5	640	57.8	51.2	69.6	55.7	31.2	56.1	66.1	Volta GPU

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