Automatic Potential Safety Hazard Evaluation System for Environment Around High-Speed Railroad Using Hybrid U-Shape Learning Architecture

Potential safety hazards (PSHs) around the high-speed railroad need to be detected and evaluated regularly and timely to ensure high-speed railroad operation safety. Unmanned aerial vehicle (UAV)-based PSH evaluation has great potential to supplement the current manual visual inspection tasks by providing better overhead views and less man-made accidents. This study presents an evaluation system for PSHs along high-speed railroad tracks. First, a novel hybrid learning architecture named UYOLO (U-shape You Only Look Once) is designed, which integrate the CSP-based backbone and detection branch to produce three scale high-level features for the object detection. Then, an innovative parsing branch inserted behind high-level layer of the structure progressively transmits context information to the shallow layer to accurately accomplish the pixel-level parsing task. Second, a new loss function using minimum point distance IoU (MPD-IoU) is designed and incorporated into the architecture to optimize the coordinate regression process of the predicted bounding boxes. Conveniently, an image-based hazard evaluation model is also developed and integrated to rate the hazard level of the detected PSHs. Finally, extensive experiments conducted on the track environment dataset established with UAV imagery indicate the proposed system can achieve a high detection rate yet remains efficient and convenient.