Deep Learning-Based Real-Time Railway Obstruction Detection

The railway sector plays a vital role in passenger transportation, economic growth, and large-scale urban development. Despite continuous improvements in signaling and control systems, railway safety remains a major concern due to frequent incidents caused by track obstructions such as unauthorized human intrusions, animals, vehicles, and miscellaneous foreign objects. Existing railway monitoring systems rely heavily on manual inspection, physical barriers, and rule-based alerts, which suffer from delayed response times, limited coverage, high maintenance costs, and reduced reliability under challenging conditions including low illumination, adverse weather, and crowded environments. This paper presents a deep learning-based real-time railway obstruction detection framework utilizing advanced image analysis and object detection techniques. Multiple state-of-the-art neural network architectures, including SSD, Faster R-CNN, RetinaNet, YOLOv3, YOLOv7, YOLOv8, YOLOv5S6, YOLOv5X6, and YOLOv9, are implemented and systematically evaluated for detecting and localizing railway obstructions. The models are trained and tested using a hybrid dataset composed of custom railway images and publicly available datasets to ensure robustness across diverse operational scenarios. Performance is assessed using standard metrics such as Mean Average Precision (mAP), precision, recall, and inference speed. Experimental results demonstrate that YOLO-based architectures, particularly YOLOv8 and YOLOv9, achieve superior detection accuracy while maintaining real-time processing capability. These findings highlight the effectiveness of YOLO-derived models as reliable solutions for enhancing railway safety and supporting intelligent transportation systems.