Rail Safe Vision: Real-Time Railway Track Hazard Detection and Signal Alert System

The RailSafe Vision project is aimed at achieving a novel solution by bringing in Computer vision and Machine learning methods to improve the safety in railway network. The system is dedicated to detecting on-track hazards in real-time as well as accurately interpreting railway signals, being able to greatly minimize chances of human errors and unforeseen obstacles. The solution makes use of machine learning models for object detection and image processing (Yolo, OpenCV) granting the system fast response times — all to help quickly alert train operators about potential obstacles. RailSafe Vision logs event data continuously, so it supports the work of post-incident investigations as well as ongoing safety improvement. This approach is putting Railtrack on a track toward better rail operations and service efficiency in Britain’s railways (Swainston 2002).