Rapid detection method for track fastener defects based on improved FasterNet and YOLOv8s

LIU Erlin; LI Tao; FENG Haizhao

doi:10.11860/j.issn.1673-0291.20250014

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.