A Survey of AI-Based Railway Track Object Detection and Alert Systems

Railway safety is affected by the intrusion of people, animals, vehicles, debris, and other foreign objects into railway track areas. Manual monitoring can be delayed and can produce human errors, while automated vision systems face challenges related to track-region localization, small or irregular objects, changing environmental conditions, false alarms, and computational cost. This survey reviews six studies addressing railway foreign-object detection and track monitoring. The reviewed methods include improved U-Net and YOLOv5s, the TOSS camera–LiDAR system, an improved LeaderRank-based monitoring-point selection and hybrid deep-learning pipeline, MSL-YOLO, MACE-Net, and Mask R-CNN-based track segmentation. Reported contributions, outcomes, and limitations are compared with attention to differences in datasets and experimental settings. The survey identifies recurring needs for diverse real-world datasets, reliable danger-zone localization, lightweight inference, consistent benchmarking, and verified end-to-end alert delivery.