An improved YOLOv8n with multi-scale feature fusion for real time and high precision railway track defect detection

Experiments on a real-world track defect dataset showed the proposed model achieved 90.2% detection precision, 90.2% mAP@0.5, and 73.2% mAP@0.5:0.95. Meanwhile, the model size was reduced to 5.2MB with 2.45M parameters. Comparative and ablation studies confirmed the complementary advantages of each module and the model's superior performance over existing lightweight detectors. The proposed model provides a robust, accurate, and efficient solution for real-time railway defect detection. It exhibits strong potential for deployment in edge AI devices and mobile inspection robots, addressing the limitations of traditional inspection methods.