BCD-YOLO: a railway perimeter foreign body intrusion detection method based on Yolov8

Abstract The railway perimeter is a key area for ensuring railway operational safety, and the intrusion of foreign objects seriously threatens the railway traffic safety. Considering the false and missed detections caused by the small size objects and the intricate background, this study proposed a detection approach called BCD-YOLO, based on YOLOv8. First, an enhanced dynamic sparse attention module was developed. It captures fine-grained details in the feature map and strengthens image context correlation, while reducing computational complexity. Second, the enhanced feature fusion module (CARAFE) was improved and T-CARAFE was constructed to replace the nearest neighbor linear interpolation method in YOLOv8, reducing the problem of feature loss during upsampling to improve the model’s capability of detecting small targets and feature catching capability. Finally, the DeepSORT object tracking algorithm was combined, which considered its trajectory and dwell time to determine whether an alarm is required, filtering out the effects of irrelevant objects in complex environments, and reducing the false alarm rate. The tracking ability of DeepSORT was further improved by introducing a shift window (Swin Transformer). In the experiment, the dataset defined a railway perimeter to simulate the actual railway perimeter environment, and only intruding objects within this perimeter were excluded. The results show that BCD-YOLO, compared with the original YOLOv8, has a precision improvement of 5.22%, recall rate improvement of 1.62%,mAP@0.5 improvement of 2.35%, and 105.5 frames per second. Combined with DeepSORT, the false detection rate is reduced by 16%, which satisfies the requirements of both accuracy and speed in railway object detection and effectively improves the problem of false and missed detections of small objects. Therefore, BCD-YOLO shows strong potential for deployment in intelligent railway monitoring systems, improving detection accuracy and reliability in complex environments while enhancing public safety by reducing intrusion-related risks.