Real-Time Detection and Recognition of Railway Traffic Signals Using Deep Learning

Abstract

Automated detection and recognition of traffic signals are of great significance in railway systems. Autonomous driving solutions are well established for urban rail transportation systems. Many metro lines in service worldwide have reached the highest grade of automation where the train is automatically operated without any staff on board. However, autonomous driving is still an open challenge for mainline trains, due to the complexity of the mainline environment. In this context, automated recognition of wayside signals can help to minimise the risk of human error owing to low visibility and fatigue. It represents a key step towards the fully autonomous train. In this article we present a deep learning based approach for the above task. The You Only Look Once (YOLOv5) is used for detection and recognition of wayside signals. A heuristic is used to recognise blinking states. We consider FRSign dataset, a large collection of over 100,000 images of traffic signals from some of the trains in French Railways. A distilled and cleaned version of the dataset curated by us is used for training. The trained network has low computational overhead and can recognise traffic signals in real time and under diverse field conditions. It has robust performance even for complex scenes having multiple signals and light sources, and in adverse circumstances such as rain and night environments. The refined version of the dataset is published as open for validation and further research and development.