Deep Arc Detection for High-Speed Railways: An Improved YOLOv5 Approach with SEC3 Attention and BiFPN Fusion

Reliable and efficient arc detection is critical for ensuring the safe operation of high-speed railways. However, existing methods often suffer from low accuracy and high computational complexity, hindering their practical application. To tackle these challenges, we propose a novel deep learning-based method that significantly improves the YOLOv5 object detection network for real-time arc detection in pantograph-catenary systems. Our approach introduces two key innovations: (1) a Squeeze-and-Excitation-based C3 (SEC3) attention module to adaptively prioritize informative features, and (2) a Bi-directional Feature Pyramid Network (BiFPN) for enhanced multi-scale feature fusion. We conduct comprehensive experiments on a diverse dataset collected from real-world scenarios, covering various challenging conditions. The results show that our improved YOLOv5 network achieves superior performance compared to state-of-the-art methods in both accuracy and efficiency. Moreover, ablation studies confirm the merits of the proposed SEC3 attention and BiFPN fusion modules in boosting performance. Our approach offers a promising solution for automatic arc detection, thereby contributing to the enhanced safety and reliability of high-speed railway systems.