Asian Journal of Research in Computer Science

Ensuring operational safety on electrified railways requires the accurate detection of small foreign objects, necessitating high-precision detection algorithms. EBSE-YOLO represents an advanced algorithm dedicated to advancing small goal recognition within electrified train settings. The detection accuracy of EBSE-YOLO improves and reduces the system load by utilising advanced techniques, which include ECA-net for small goal prioritisation, BiFPN-inspired cross-level feature fusion and SPD-Conv for detail extraction and the EIOU loss characteristic for dimension alignment. Testing with different YOLOv5 configurations and Ghost CNN supplement approaches enabled the suggested approach to reach outstanding performance. EBSE-YOLO reaches a mAP precision of 97% in initial monitoring, but the system integrates YOLOv5 with Ghost CNN to surpass 98% mAP levels. EBSE-YOLO contributes benefits that extend beyond basic performance indicators because it creates substantial impacts on railway safety, together with management oversight. EBSE-YOLO applies modern model designs coupled with recognition techniques to enhance tiny goal detection capabilities and establish a system for ongoing railway safety improvement innovations. The research develops foundational guidelines that upcoming software for object detection uses to enhance railway safety operations in complex environments. The model developers are optimising it continuously to maximise performance for embedded machinery and drones that need to deploy it in real-time surveillance operations for railway safety.