Railway obstacle intrusion detection and risk assessment based on MSIA-YOLOv8 and DALNet

Railway obstacle intrusion detection is crucial for safe train operation, especially as high-speed train continues to develop rapidly. However, existing algorithms still have limitations in detecting distant small objects, adapting to low-light conditions and adverse weather. Moreover, most studies overlook the importance of assessing the risk levels of obstacles. To address these issues, a railway obstacle intrusion detection and risk assessment method based on MSIA-YOLOv8 and DALNet is proposed in this paper. Through comprehensive improvements in network architecture and loss function (Inner-Focal-CIoU), MSIA-YOLOv8 improves the accuracy of multi-scale obstacle detection, especially for distant small objects. The Frequency Domain Aggregation and Enhancement (FDAE) further enhances detection accuracy and robustness in dark and bad-weather conditions while ensuring real-time performance. The algorithm achieves 97.3% mAP with a speed of 137 FPS on the railway dataset. It outperforms YOLOv8 and meets the speed, accuracy and computational requirements of railway scenarios. Furthermore, the integration of DALNet enables the detection of railway obstacle intrusion, as well as the classification and assessment of their risk levels.