摘要
[目的]站台门间隙异物检测环节对地铁运营安全有影响,故有必要研究一种新型的地铁站台门防夹检测系统,使未来的FAO(全自动运行)系统更加安全与高效。[方法]采用视频和激光雷达算法融合技术,提出了结合视频图片识别和雷达点云数据的双重判据AI检测策略,创新性地采用了PointNet算法架构来进行地铁站台门间隙异物的检测,实现摄像头视频辅助激光雷达工作模式。若被检测间隙出现异物,则报警和视频联动,第一时间捕捉报警现场视频。利用多维深度学习方法,降低误判概率。[结果及结论]在系统设计中,提出传感器交叉叠装分层安装方法,实现间隙异物冗余检测功能;通过交叉互检机制,有效提高了检测装置的冗余性和可靠性;使用2D传感器实现3D检测效果。所研制系统为地铁信号系统提供安全联锁信号,提供报警信息给综合监控系统,并推送手环报警信息给现场运行人员。使地铁站台门间隙异物检测更加准确可靠,为地铁的全自动运行提供安全保障。
[Objective]The detection of foreign objects in platform door gap is critical to metro operational safety.Therefore,it is essential to develop a new anti-clamping detection system for metro platform door,enhancing the safety and efficiency of future FAO(fully automatic operation)systems.[Method]Based on the video and LiDAR algorithm fusion technology,a dual-criterion AI detection strategy that combines video image recognition with LiDAR point cloud data is proposed.PointNet algorithm framework is innovatively adopted for the detection of foreign objects in metro platform door gap,implementing a camera video assisted LiDAR working mode.In the event of foreign object detection in door gap,the system triggers an alarm-video synergistic operation and initiates video capture of the incident site immediately.The use of multi-dimensional deep learning techniques reduces the probability of false alarms.[Result&Conclusion]In system design,a cross-stacking layered sensor installation method is proposed,enabling the redundant detection function of foreign objects in platform door gaps.The cross-verification mechanism significantly enhances the redundancy and reliability of the detection device,and using 2D sensors to achieve 3D detection effects.The developed system provides safety interlocking signals to metro signaling system,sends alarm information to the integrated monitoring system,pushing wristband alerts to on-site operation personnel.This system ensures more accurate and reliable detection of foreign objects in platform door gaps,offering safety support for FAO.
作者
于庆广
王石
高泊楠
陈宇轩
萧成博
刘又齐
王玉瑾
赵明
李乐
蔡冠之
YU Qingguang;WANG Shi;GAO Bonan;CHEN Yuxuan;XIAO Chengbo;LIU Youqi;WANG Yujin;ZHAO Ming;LI Le;CAI Guanzhi(Electrical Engineering Department,Tsinghua University,100084,Beijing,China;Beijing Guanfenghang Safety Technology Co.,Ltd.,100085,Beijing,China)
出处
《城市轨道交通研究》
北大核心
2024年第10期193-198,共6页
Urban Mass Transit
基金
北京市大学生创新创业训练计划项目(S202210003076)。