A Review of Deep Learning Applications for Railway Safety

Railways speedily transport many people and goods nationwide, so railway accidents can pose immense damage. However, the infrastructure of railways is complex that its maintenance challenging expensive. Therefore, using artificial intelligence for safety has attracted researchers. This paper examines applications safety, mainly focusing on deep learning approaches. first introduces methods widely used safety. Then, we investigated classified earlier studies into four representative application areas: (1) (catenary, surface, components, geometry), (2) train body bogie (door, wheel, suspension, bearing, etc.), (3) operation (railway detection, railroad trespassing, wind risk, running (4) station (air quality control, accident prevention, etc.). We present fundamental problems popular approaches each area. Finally, based literature reviews, discuss opportunities challenges