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