Improved YOLOv8 for High-Precision Detection of Rail Surface Defects on Heavy-Haul Railways

Abstract

The complex infrastructure and harsh conditions of heavy-haul railways result in frequently and rapidly deteriorating rail surface defects. Accurate detection of these defects is essential. To solve the problem of low detection precision caused by complex background interference, significant variation in defect scales, and similar features between different types of defects, a high-precision rail surface defect detection method for heavy-haul railways based on an improved YOLOv8 is proposed. First, the original grayscale images are preprocessed to reduce background noise interference. Then, the designed scale variation adaptation module is introduced to mitigate the impact of significant scale variations in the target defects. Additionally, a bidirectional feature pyramid network is incorporated to enhance feature fusion effectiveness. Furthermore, a small target detection head is introduced to improve the detection performance of small-scale defects. Lastly, network performance is optimized by replacing the original loss function with wise-intersection over union. Experimental results demonstrate that the improved model achieves a mean average precision at 50% intersection over union (mAP50) value of 0.975, representing a 4.13% improvement in precision and a 7.75% increase in recall compared to the baseline model. The improved model effectively detects typical defects such as spalling, shelling, and corrugation, providing valuable technical support for field maintenance personnel.