基于改进YOLOv5s模型的电动三轮车检测  

Electric Tricycle Detection Based on Improved YOLOv5s Model

作　　者：欧晓放 韩凤春 田竞 唐继杰 杨政陶 Ou Xiaofang;Han Fengchun;Tian Jing;Tang Jijie;Yang Zhengtao(School of Traffic Management,People’s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学交通管理学院,北京100038

出　　处：《激光与光电子学进展》2025年第2期204-212,共9页Laser & Optoelectronics Progress

基　　金：中国人民公安大学交通管理工程双一流创新研究专项(2023SYL09);中国人民公安大学拔尖创新人才培养经费支持研究生科研创新一般项目(2023YJSKY015)。

摘　　要：针对我国道路交通管理工作中的电动三轮车目标检测问题,以及当前检测模型在小目标检测和实时性上的不足,提出了一种基于改进YOLOv5s模型的检测方法。对原始的YOLOv5s模型进行改进,增加小目标检测头,引入结合高效加性注意力机制的Transformer结构,结合城市道路场景构建数据集。实验结果显示,改进模型在精确度、召回率和平均精度均值(mAP@0.5)等性能指标上分别提升了0.67、2.68、5.78百分点。同时,模型在帧率上达到了92 frame/s,表现出了良好的处理能力,能够满足实际道路交通场景下的实时检测需求。To address the problems related to target detection of electric tricycles in road traffic management in China and the shortcomings of current detection models in small target detection and real-time performance,this study proposes a detection method based on an improved YOLOv5s model.The original YOLOv5s model is first improved by adding a small object detection head and by introducing a Transformer structure that combines an efficient additive attention mechanism,and then a dataset based on urban road scenes is built.The model is improved in terms of accuracy,recall,and mean average precision(mAP@0.5)by 0.67%,2.68%,and 5.78%,respectively.The model also achieves a frame rate of 92 frame/s and demonstrates good processing capabilities,thus meeting the real-time detection requirements for actual road traffic situations.

关 键 词：机器视觉 目标检测 YOLOv5算法 Transformer结构 电动三轮车 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]