YOLOv5监测算法下轨道交通应急预警感知系统构建  

Construction of rail transit emergency early warning perception system under YOLOv5 detection algorithm

作　　者：雷斌 闫浪浪 余华 温岩 张亮 李哲旭 LEI Bin;YAN Langlang;YU Hua;WEN Yan;ZHANG Liang;LI Zhexu(School of Civil Engineering,Xi'an University of Architecture and Technology,Xi'an Shaanxi 710055,China;Baoji Municipal Transportation Bureau,Comprehensive Planning Division,Baoji Shaanxi 721004,China;Operating Branch,Xi'an Rail Transit Group Co.,Ltd.,Xi'an Shaanxi 710018,China)

机构地区：[1]西安建筑科技大学土木工程学院,陕西西安710055 [2]宝鸡市交通运输局综合规划科,陕西宝鸡721004 [3]西安市轨道交通集团有限公司运营分公司,陕西西安710018

出　　处：《中国安全科学学报》2024年第9期225-233,共9页China Safety Science Journal

基　　金：陕西省科学技术厅社会发展领域项目(2021SF-486);陕西省交通科技项目(20-05R)。

摘　　要：为减少城市轨道交通站点中因大客流状态下应急预警系统的滞后性而发生的行人安全问题,选取YOLOv5算法预测客流信息,并利用人工神经网络(ANN)模型来构建城市轨道交通应急预警感知系统。首先,通过模型训练超参数优化和先验框参数优化改进YOLOv5算法;然后,通过预警指标选取、权重分析和阈值界定设计应急预警感知系统;最后,采用Matlab软件构建基于ANN的自组织竞争网络应急预警模型,将优化后的YOLOv5算法采集的数据通过计算代入应急预警感知系统中,通过试验验证应急预警感知系统。结果表明:优化后的YOLOv5算法相较原算法,城市轨道交通大客流状态下行人目标监测精确度提高7.04%;由优化后的YOLOv5算法所采集到的行人数据代入构建的应急预警感知系统后得到的判断结果与实际预警等级一致,证明了该系统的可实施性和有效性,有助于提高城市轨道交通应急预警水平。In order to reduce the pedestrian safety problems caused by the lag of the emergency warning system in urban rail transit stations under large passenger flow conditions,the YOLOv5 algorithm was selected to predict passenger flow information.The artificial neural network(ANN)model was used to construct the urban rail transit emergency warning perception system.Firstly,the YOLOv5 algorithm was improved by optimizing the model training hyperparameters and prior frame parameters.Then,the emergency warning perception system was designed by selecting warning indicators,weight analysis and threshold definition.Finally,the self-organizing competitive network emergency warning model based on ANN was constructed by using Matlab software.The data collected by the optimized YOLOv5 algorithm were substituted into the emergency warning perception system through calculation,and the emergency warning perception system was verified by experiments.The results show that the optimized YOLOv5 algorithm can improve the accuracy of pedestrian target monitoring under large passenger flow conditions of urban rail transit by 7.04%.The judgment results obtained by substituting the pedestrian data collected by the optimized YOLOv5 algorithm into the constructed emergency warning perception system are consistent with the actual warning level,which proves the feasibility and effectiveness of the system and helps to improve the emergency warning level of urban rail transit.

关 键 词：YOLOv5算法 监测算法 轨道交通 应急预警感知系统 目标监测 超参数 

分 类 号：X924.4[环境科学与工程—安全科学]