文献类型：会议论文

英文题名：Research on the Application of YOLO v3 in Railway Intruding Objects Recognition

作者：Ma, Yongtian[1]; Fang, Jianjun[1]; Zhao, Jiaxiang[1]; Zhang, Qiushi[1]

第一作者：Ma, Yongtian

机构：[1] Beijing Union University, College of Urban Rail Transit and Logistics, Beijing, Beijing, China

第一机构：北京联合大学城市轨道交通与物流学院

会议论文集：2022 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2022

会议日期：June 24, 2022 - June 26, 2022

会议地点：Dalian, China

语种：英文

外文关键词：Intrusion detection - Object detection - Railroad accidents - Railroad transportation - Railroads

摘要：In order to detect foreign objects intruding into the track and prevent foreign objects from causing railroad safety accidents, the track foreign object intrusion detection algorithm is investigated. For the specific application scenario of railway foreign object intrusion, the enhanced YOLOv3 high speed railway foreign object detection network is proposed to improve the ability of using picture features and detection effect, and the average detection accuracy reaches 79.2% with slightly reduced detection speed, which is 4.4% higher than the original network. The enhanced YOLOv3 railroad foreign object intrusion detection network can effectively improve the detection accuracy of targets at different scales. ? 2022 IEEE.