Studying the performance of YOLOv11 incorporating DHSA BRA and PPA modules in railway track fasteners defect detection

Abstract

With the development of railway transportation and the advancement of deep learning, object detection algorithms are increasingly replacing manual inspection of track fasteners. However, current algorithms struggle with low accuracy in complex weather conditions or low-contrast backgrounds. To address this, we propose a track fastener defect detection algorithm based on YOLOv11 (You Only Look Once).First, we incorporate the DHSA (Dynamic-range Histogram Self-Attention) module into the backbone network of YOLOv11 to enhance noise robustness. Second, we introduce the BRA (Bi-Level Routing Attention) sparse attention mechanism into the neck network for improved efficiency. Finally, we add the PPA (Parallelized Patch-Aware Attention) module to the original neck network to enhance multi-scale feature extraction, specifically for small object detection.To validate the model, we created a dataset and conducted experiments. The experimental results show that YOLO-DRPA achieves a mAP@0.5 of 94.6% and a mAP@0.5:0.95 of 80.7%, marking improvements of 1.8% and 4.0% over YOLOv11n, respectively. The model also demonstrates competitive performance compared to other popular object detection algorithms, highlighting its potential to improve both detection accuracy and efficiency.