Foreign Object Detection in Railway Tracks: A Survey

Railway transportation is highly vulnerable to accidents caused by foreign objects obstructing tracks, which can endanger passenger safety and cause significant delays, damage to infrastructure, and interruptions in train services. Conventional monitoring methods are inefficient due to their dependence on manual supervision and poor adaptability in adverse conditions. This paper presents a survey of foreign object detection techniques in railway tracks using computer vision and deep learning approaches. In the proposed system, images captured under adverse conditions are first enhanced using image enhancement techniques and then passed to the YOLO-based object detection module, whereas normal images are directly processed for object detection. Once an obstruction is detected, automated alerts containing object type, image, risk level, location, and time are transmitted through a mobile application, supported by a backend for incident logging and rapid communication. By integrating computer vision and real-time notification systems, the solution offers a reliable and scalable approach to improving railway safety and ensuring uninterrupted train services