广西科学院学报2023，Vol.39Issue(4)：471-478,8.DOI:10.13657/j.cnki.gxkxyxb.20231226.014
基于改进YOLOV7与StrongSORT算法的列车司机手比行为检测
Gesture Behavior Detection of Train Drivers Based on Improved YOLOV7 and StrongSORT
摘要
Abstract
The standardization of train driver's driving behavior directly affects the running safety and state of train operation.At present,the detection of train driver's gesture behavior is insufficient.In order to effective-ly detect the gesture behavior of train drivers,this article uses the images of drivers'crew work collected by the EMU simulation driving system,combined with the You Only Look Once Version 7(YOLOV7)neural network model using the fusion attention mechanism and the Strong Simple Online and Realtime Tracking(StrongSORT)algorithm,five kinds of gesture behaviors of EMU drivers during driving were tested.The ex-perimental results show that the algorithm in this article can effectively improve the detection effect of differ-ent types of gesture behaviors when the train driver is working.The detection accuracy rate is increased by 1.2%on average,and the detection recall rate is increased by 1.9%on average.The algorithm proposed in this article will help to improve the effect of daily training and work evaluation of train drivers by railway col-leges and locomotive departments,and improve the safety in the process of train operation.关键词
列车司机/乘务作业/行为检测/YOLOV7/StrongSORT/注意力机制Key words
train drivers/crew work/behavior detection/YOLOV7/StrongSORT/attention mechanism分类
数理科学引用本文复制引用
宋吉超,黄伟,陈振棠,周成才..基于改进YOLOV7与StrongSORT算法的列车司机手比行为检测[J].广西科学院学报,2023,39(4):471-478,8.基金项目
柳州铁道职业技术学院2022年度校级立项项目"基于人工智能技术的列车司机行为监测系统"(2022-KJB14)资助. （2022-KJB14）
