Rail Fastener Fault Detection Based on Enhanced YOLOv7 Model

Railway networks are crucial for transportation infrastructure, and ensuring their safety and efficiency is of utmost importance. This paper proposes an efficient computer vision-based railway detection system that incorporates state-of-the-art techniques for fastener fault detection. By utilizing an improved version of the YOLOv7 algorithm and the novel Self Improved-Owl Optimization algorithm, the system accurately locates and recognizes the states of rail fasteners, enabling efficient fault detection. Experimental evaluations demonstrate significant improvements in detection accuracy and computational efficiency compared to traditional methods. The system's ability to provide precise localization and state recognition of fasteners contributes to the overall safety and efficiency of railway networks, reducing maintenance costs and enhancing transportation reliability. The proposed model achieves an impressive accuracy of 97%, indicating its effectiveness in accurately detecting and recognizing rail fastener faults. This high level of accuracy demonstrates the robustness and reliability of the model, making it a valuable tool for railway maintenance teams.