摘要
为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)、FCHD(Fully Convolutional Head Detector)、CSRNet(Network for Congested Scene Recognition)模型作为基模型进行集成,最终实现客流统计、拥挤度分析和辅助清客等功能。利用北京城轨某线路列车的监控图像数据进行实验,结果表明,与其他各基模型相比,该系统采用的模型检测效果更佳,有效提升了检测精度,丰富了可适用的检测场景。
In order to solve the problems of low detection accuracy and single application scenario in current urban rail transit train passenger flow analysis,this paper designed an intelligent passenger flow analysis system for urban rail transit trains based on heterogeneous ensemble learning method.The system was based on cloud edge collaboration architecture and adopted the grouping Voting method to integrate YOLOv5s(You Only Look Once v5s),FCHD(Full Convolutional Head Detector),and CSRNet(Network for Congested Scene Recognition)models as the base models,ultimately implemented functions such as passenger flow statistics,congestion analysis,and auxiliary passenger clearance.The experiment was conducted using monitoring image data of a train on a certain line of Beijing urban rail transit.The experimental results show that the model used in the system has better detection performance compared to other basic models,which effectively improves detection accuracy,and enriches applicable detection scenarios.
作者
张强
宫玉昕
张馨
蔡晓蕾
郑军
ZHANG Qiang;GONG Yuxin;ZHANG Xin;CAI Xiaolei;ZHENG Jun(Beijing Metro Construction Administration Corporation Limited,Beijing 100068,China;Institute of Computing Technologies,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;Beijing Jingwei Information Technologies Co.Ltd.,Beijing 100081,China)
出处
《铁路计算机应用》
2023年第7期73-78,共6页
Railway Computer Application
基金
中国铁道科学研究院集团有限公司科研项目(2021YJ285)。
关键词
智能客流分析
视频监控
异质集成学习
基模型
分组Voting方法
intelligent passenger flow analysis
video surveillance
heterogeneous ensemble learning
base model
grouped Voting method
