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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (2): 249-260    DOI: 10.3785/j.issn.1008-973X.2025.02.003
    
Research progress of YOLO detection technology for traffic object
Hongzhao DONG(),Shaoxuan LIN,Yini SHE
ITS Joint Research Institute, Zhejiang University of Technology, Hangzhou 310023, China
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Abstract  

The development and research status of YOLO algorithm in traffic object detection were systematically summarized from the perspective of the three core elements of 'people-vehicle-road' in order to comprehensively analyze the important role of YOLO (You Only Look Once) algorithm in improving traffic safety and efficiency. The commonly used evaluation indexes of YOLO algorithm were outlined, and the practical significance of these indexes in traffic scenarios was elaborately expounded. An overview of the core architecture of YOLO algorithm was provided, its development process was traced, and the optimization and improvement measures in each version iteration were analyzed. The research status and application scenarios of YOLO algorithm for traffic object detection were sorted out and discussed from the perspective of the three traffic objects 'people-vehicle-road'. The limitations and challenges of YOLO algorithm in traffic object detection were analyzed, and corresponding improvement methods were proposed. Future research focuses were anticipated, providing a research reference for the intelligent development of road traffic.

Key words： YOLO algorithm      object detection      computer vision      traffic object      traffic safety     
Received: 06 February 2024      Published: 11 February 2025
CLC:  TP 391  
Fund:  浙江省自然科学基金资助项目（LMS25F030007）；浙江省“尖兵”“领雁”研发攻关计划资助项目（2024C01180）.
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Hongzhao DONG
Shaoxuan LIN
Yini SHE
Cite this article:

Hongzhao DONG,Shaoxuan LIN,Yini SHE. Research progress of YOLO detection technology for traffic object. Journal of ZheJiang University (Engineering Science), 2025, 59(2): 249-260.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.02.003     OR     https://www.zjujournals.com/eng/Y2025/V59/I2/249


交通目标YOLO检测技术的研究进展

为了综合分析YOLO（You Only Look Once）算法在提升交通安全性和效率方面的重要作用，从“人-车-路” 3个核心要素的角度，对YOLO算法在交通目标检测中的发展和研究现状进行系统性地总结. 概述了YOLO算法常用的评价指标，详细阐述了这些指标在交通场景中的实际意义. 对YOLO算法的核心架构进行概述，追溯了该算法的发展历程，分析各个版本迭代中的优化和改进措施. 从“人-车-路”3种交通目标的视角出发，梳理并论述了采用YOLO算法进行交通目标检测的研究现状及应用情况. 分析目前YOLO算法在交通目标检测中存在的局限性和挑战，提出相应的改进方法，展望未来的研究重点，为道路交通的智能化发展提供了研究参考.


关键词： YOLO算法,  目标检测,  计算机视觉,  交通目标,  交通安全 
Fig.1 Development timeline of YOLOv1-v8 series algorithm
Fig.2 Schematic diagram of YOLOv1-v3 algorithm network structure
Fig.3 Schematic diagram of YOLOv4-v8 algorithm network structure
文献研究方向数据集YOLO原型改进方式P/%mAP/%v/(帧·s?1)
文献[39]车载行人检测自建数据集YOLOv3
YOLOv4
YOLOv4-tiny		结合Wasserstein
距离损失		—
—
—		98.57
98.19
80.39		3.858 6
3.404 9
22.592 8
文献[41]监控行人检测自建数据集YOLOv4引入SPP-Net结构—84.47—
文献[43]行人跟踪PD-2022YOLOv3结合DeepSort算法72.02—33
文献[45]行人行为模式识别自建数据集YOLOv8结合运动特征分析———
文献[46]人群密度估计和客流计算自建数据集YOLOv3结合DeepSort算法、
Voronoi图		97.87——
文献[48]驾驶员疲劳检测自建数据集YOLOv3—94.66(眨眼)、
95.99(打哈欠)		——
文献[49]驾驶员疲劳检测自建数据集、NTHU-DDDYOLOv5增加特征采样次数，
结合BiFPN结构		—99.40100
文献[50]驾驶员分心检测IR dataset of HNUST and HNUYOLOv7结合全局注意机制（GAM），
通道扩展（CE）数据增强		—73.60156
文献[52]乘客客流检测自建数据集YOLOv3结合Cam-shift算法89.71——
Tab.1 Application of YOLO algorithm with 'Human' as detection object in traffic target
文献研究方向数据集YOLO原型改进方式P/%mAP/%v/(帧·s?1)
文献[53]机动车检测BDDK100YOLOv5将主干网络替换为ResNet50，
引入自适应比例系数		86——
文献[55]非机动车检测自建数据集YOLOv3改进特征融合结构，采用GIOU损失—70.8—
文献[56]应急车辆检测自建数据集YOLOv5结合VGGNet算法—95.7—
文献[58]3D车辆检测KITTI[67]YOLOv2改为3D目标检测，引入E-RPN—54.7750.4
文献[59]车辆跟踪自建数据集YOLOv3结合边界框距离计算与相似性对比95.45——
文献[60]交通密度分析BIT-Vehicle Dataset[68]、
GRAM-RTM[69]		YOLOv4结合CFNN卷积模糊神经网络90.459930
文献[62]车辆速度估计VS13[70]YOLOv5结合1D-CNN算法———
文献[63]车辆异常行为检测自建数据集YOLOv3结合质心跟踪算法100——
文献[64]交通事故检测自建数据集YOLOv3结合MOSSE跟踪算法、ViF描述符93——
Tab.2 Application of YOLO algorithm with 'Vehicle' as detection object in traffic target
文献研究方向数据集YOLO原型改进方式PAPmAP
文献[71]交通标志检测CCTSDB2021YOLOv4引入BECA注意力机制、密集SPP模块、k-means++聚类算法96.62—92.77
文献[73]交通灯检测BDD100K、
Bosch		YOLOv5引入ACBlock、SoftPool、DSConv模块—
—		74.3
84.4		—
—
文献[75]道路划线检测自建数据集YOLOv2结合模板匹配技术100——
文献[76]停车位检测自建数据集YOLOv5引入SPPF模块、GELU激活函数、CA机制、圆形平滑标签——70.72
文献[81]路面障碍检测TACO[85]YOLOv5———24.77
文献[82]道路损坏检测RDD2020[86](自主拓展)YOLOv5将主干网络替换为Shuffle-ECANet59.2—57.6
Tab.3 Application of YOLO algorithm with 'Road' as detection object in traffic target %
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