Abstract: The high-speed rail contact network is a critical conductor in the electrified railway system,and ensuring the proper functioning of its wires is crucial for maintaining the stable operation of the railway.Traditional manual inspection methods are inefficient and prone to oversight.With the rapid development of deep learning technologies,the use of computer vision techniques for automated detection has become an urgent necessity.In response to the challenges of detecting the state of wires in high-speed rail contact networks under various complex outdoor backgrounds and diverse environmental conditions(such as night and day),this paper proposes a wire state detection algorithm based on the combination of a Detail Enhancement Fusion Module(DEFM) and YOLOv8.By incorporating spatial and channel attention mechanisms,the algorithm fuses infrared and visible light images,introducing multimodal fusion and the Shuffle Attention mechanism.Experiments conducted on a real dataset demonstrate the mo-del’s significant improvement in performance metrics such as detection accuracy and recall rate.The results show that the improved algorithm increases the recall rate by 0.94% and mAP by 2.09% compared to the original algorithms.Practical tests indicate that the DEFM-YOLOv8-based detection model performs effectively in detecting wires in the high-speed rail contact network,regardless of whether the environment is nighttime or daytime,even under complex backgrounds.

Key words: High-speed rail contact network, YOLOv8, DEFM, Infrared-visible light image fusion, Attention mechanism, Object detection