基于深度学习的地铁车站站台层实时客流检测应用研究  

Application Research on Passenger Flow Real-time Detection of Subway Station Platform Based on Deep Learning

作　　者：吴俊演 刘霞[1] 李雅卓 WU Junyan;LIU Xia;LI Yazhuo(School of Intelligent Manufacturing,Jianghan University,Wuhan 430056,Hubei,China)

机构地区：[1]江汉大学智能制造学院,湖北武汉430056

出　　处：《江汉大学学报（自然科学版）》2023年第5期67-74,共8页Journal of Jianghan University：Natural Science Edition

基　　金：湖北省汽车制动管智能产线关键技术科技创新团队项目;江汉大学校级科研项目资助计划(2023KJZX37)。

摘　　要：将基于深度学习的目标检测算法YOLO-V5与多目标追踪算法DeepSORT相结合,实现了地铁车站站台层行人客流信息的实时检测与统计。首先,为减少因行人相互遮挡导致的错检和漏检问题,将传统的行人全身检测改为头肩部检测;然后,训练DeepSORT中的ReID模型,只提取行人头肩部特征,从而减少因追踪过程行人ID的频繁切换而导致的计数不准确问题;最后,将优化好的行人检测追踪模型应用到地铁站台层客流检测中,根据实际应用场景提取并统计不同客流信息。结果表明,该模型能有效检测站台拥挤程度,并能对站台出入口的上下行人数进行统计,准确率达到86%,平均FPS为35,能够满足客流信息实时检测的应用需求。In this paper,YOLO-V5,a target detection algorithm based on deep learning,was combined with DeepSORT,a multi-target tracking algorithm,to achieve real-time detection and statistics of pedestrian flow information at the platform level of subway stations.Firstly,to reduce the problem of false detection and missed detection caused by pedestrians'mutual occlusion,the traditional pedestrian whole-body detection was changed to pedestrian head and shoulder detection.Then,the ReID model in DeepSORT was trained to extract only the head and shoulder features of pedestrians,to reduce the problem of inaccurate counting caused by frequent switching of pedestrian IDs in the tracking process.Finally,the optimized pedestrian detection and tracking model was applied to the subway platform level passenger flow detection,and different passenger flow information was extracted and counted according to the actual application scenario.The results showed that the model could effectively detect the degree of platform congestion,and count the number of people going up and down at the entrance and exit of the platform.The accuracy rate was 86%and the average FPS was 35,which could meet the application requirements of realtime detection of passenger flow information.

关 键 词：YOLO-V5 目标检测 DeepSORT 目标追踪 客流检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]