Detection System Design and Implementation for Foreign Objects in Automatic Platform Door Gap

Abstract

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.

Keywords

• platform door
• lidar and video
• fusion algorithm
• automatic detection