A railway fastener inspection method based on lightweight network

Abstract

Abstract To ensure the safety of train operations, regular inspection and maintenance of railway fastener is crucial. Currently, computer vision-based fastener inspection has become the primary method. However, to improve detection accuracy, model complexity has been continuously increasing, leading to a decrease in detection efficiency and seriously affecting real-time fastener inspection task. To address this issue, we propose a fastener inspection method based on lightweight network. First, we introduce YOLOv5n-Faster, where this model selects YOLOv5n as the base network for fastener localization and uses partial convolution (PConv) from FasterNet for lightweight design, while using CoordConv to improve localization accuracy, thereby achieving efficient fastener localization task. Then, we perform lightweight design on the overall network structure and Block module of the ConvNeXt-T to propose the fastener state inspection model, Light ConvNeXt, for rapid fastener classification. Experimental results show that our proposed fastener localization model achieves a mean average precision (mAP) of 84.7%, a detection speed of 161 FPS. The fastener state inspection model achieves an accuracy of 99.7%.