Research on Visual Detection of Intruding Foreign Objects in Rail Transit

Foreign object intrusion detection is one of the important means to ensure the safe operation of rail transit. The distance measurement of the intruding foreign object is conducive to reminding the train crew to take corresponding measures in time. This paper proposes a distance detection algorithm for intruding foreign objects based on monocular vision. Firstly, corner detection and threshold segmentation methods are used to obtain the image coordinates of a black-and-white checkerboard. Secondly, regression analysis is employed to establish a conversion model between image coordinates and world coordinates. Finally, an object detection algorithm based on the YOLOv10 network is used for image recognition to obtain the coordinates of the intruding foreign objects in the image coordinate system. The distance information of the intruding objects is detected. Experimental validation shows that the distance of the intruding objects has an error range of -5 to +6 cm in the X-axis direction and -9 to +16 cm in the Y-axis direction, demonstrating high accuracy.