北京交通大学学报2025，Vol.49Issue(6)：64-74,11.DOI:10.11860/j.issn.1673-0291.20250014
基于改进FasterNet和YOLOv8s的轨道扣件缺陷快速检测方法
Rapid detection method for track fastener defects based on improved FasterNet and YOLOv8s
摘要
Abstract
To address the complex and diverse characteristics of track fastener defects,as well as the low efficiency and high missed-detection rates of traditional detection methods,this study proposes a lightweight detection model,FPSI-YOLOv8s,based on the YOLOv8s framework.First,to reduce model complexity,FasterNet,featuring higher processing speed and fewer parameters,is adopted to replace the CSPDarkNet53 backbone in YOLOv8s for defect feature extraction.Second,the C2f mod-ule in the YOLOv8s neck is redesigned using Position-aware Recurrent Convolution(ParConv)to form a new FasterBlock module,enabling multi-scale feature fusion and further model lightweighting.Third,a Spatial Group-wise Enhance(SGE)attention mechanism is integrated after the SPPF layer to enhance the model's sensitivity to defect features and mitigate accuracy degradation.Finally,the Inner-IoU loss function replaces CIoU to improve detection performance for objects of varying scales and shapes,while refined quality evaluation and gradient-gain strategies further enhance model robustness.Experimental results show that the improved model reduces model size by 29.78%,and decreases computational cost and parameter count by 29.93%and 30.46%,respectively,with only a 0.7%decrease in detection accuracy.These results demonstrate that the proposed model achieves sig-nificant lightweighting and improved operational efficiency while maintaining high accuracy,indicating strong application potential for rapid inspection of track fasteners.关键词
YOLOv8s/轻量化/轨道扣件/位置感知循环卷积/空间分组增强注意力机制Key words
YOLOv8s/lightweight/rail fastener/Position-aware Recurrent Convolution(ParConv)/Spatial Group-wise Enhance(SGE)attention mechanism分类
信息技术与安全科学引用本文复制引用
刘二林,李涛,冯海照..基于改进FasterNet和YOLOv8s的轨道扣件缺陷快速检测方法[J].北京交通大学学报,2025,49(6):64-74,11.基金项目
国家自然科学基金(72171106) National Natural Science Foundation of China(72171106) （72171106）
