基于YOLOv5s模型的地铁列车车顶关键部件检测算法研究  

Roof key components of metro vehicles detection algorithm based on YOLOv5 model

作　　者：张慧飞 姜汇川 刘宁 李洪 ZHANG Huifei;JIANG Huichuan;LIU Ning;LI Hong(Guangzhou Branch,Shanghai Engineering Group Co.Ltd.of CRSC,Guangzhou 510663,China;School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]中国铁路通信信号上海工程局集团有限公司广州分公司,广州510663 [2]南京理工大学自动化学院,南京210094

出　　处：《铁路计算机应用》2024年第12期1-7,共7页Railway Computer Application

基　　金：国家重点研发计划项目(2022YFB4300601)。

摘　　要：针对目前地铁列车车顶部件检查主要依靠人工,劳动强度大、漏检率高等问题,提出了一种基于YOLOv5s模型的地铁列车车顶关键部件检测算法。考虑现场算力不足的实际情况,对YOLOv5s模型进行轻量化设计,将YOLOv5s模型Backbone中的C3模块替换为Ghost_C3模块,并改用Ghost卷积取代YOLOv5s模型中的传统卷积,从而降低模型复杂度和计算量;为补偿轻量化设计带来的模型性能降低的损失,在Ghost_C3模块中引入CA注意力机制,加强对关键部件的特征感知,从而提升模型精度。实验结果表明,改进的YOLOv5s模型每秒传输帧数为102.04 f/s,mAP为97.98%,Pars为4.47 MB,FLOPs为10.2 GB,相比于原有YOLOv5s模型,mAP提升1.36%,Pars减少33.98%且FLOPs减少36.65%,所提算法能够为后续的地铁列车车顶关键部件服役状态辨识提供技术支撑。This paper proposed a key component detection algorithm for metro train roof based on YOLOv5s model to address the current problems of manual inspection,high labor intensity,and high missed detection rate in metro train roof components.Considering the practical situation of insufficient on-site computing power,the paper presented a lightweight design for the YOLOv5s model,replaced the C3 module in the YOLOv5s model Backbone with the Ghost-C3 module,and used Ghost convolution instead of traditional convolution in the YOLOv5s model to reduce model complexity and computational complexity.To compensate for the loss of model performance caused by lightweight design,the paper introduced a CA attention mechanism in the Ghost-C3 module to enhance feature perception of key components and improve model accuracy.The experimental results show that the improved YOLOv5s model has a frame rate of 102.04 f/s,mAP of 97.98%,Pars of 4.47 MB,and FLOPs of 10.2 GB.Compared with the original YOLOv5s model,mAP has increased by 1.36%,Pars has decreased by 33.98%,and FLOPs has decreased by 36.65%.The proposed algorithm can provide technical support for identifying the service status of key components on the roof of subway trains in the future.

关 键 词：YOLOv5s 轻量化 注意力机制 地铁车顶 关键部件 目标识别 

分 类 号：U231.94[交通运输工程—道路与铁道工程] TP39[自动化与计算机技术—计算机应用技术]