Intelligent Obstacle Detection And Warning System For Railroad Crossings Using Yolov8 And Bytetrack With Signal-Triggered Response
DOI:
https://doi.org/10.31294/jtk.v12i1.11049Abstract
Railroad level crossings remain a critical safety concern due to the high incidence of collisions involving vehicles and pedestrians. This study proposes an intelligent, vision-based obstacle detection and warning system that integrates the YOLOv8 object detection model with the ByteTrack multi-object tracking algorithm. Designed for real-time operation on crossing surveillance video, the system can detect and track vehicles, pedestrians, and foreign objects within the railway zone. Consistent detection is coupled with a signal-triggered response mechanism, enabling early warnings to operators or automated gate control systems, particularly as trains approach. Evaluation on a diverse video dataset—captured under varying lighting and weather conditions demonstrates that the integration of YOLOv8 and ByteTrack improves tracking consistency, reduces false negatives, and maintains latency below 50 ms per frame. This research advances intelligent transportation safety systems and offers a vision-based solution that can be integrated into existing railway infrastructure.
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