Lightweight detection method for track fastener status based on improved YOLOv8s
Railway infrastructure is continuously impacted by vehicle loads and external environmental factors,causing issues such as the loss,displacement,and damage of track fasteners along railway lines.These problems pose significant threats to the safe operation of railways.To address the low de-tection efficiency,high omission rates,and lack of real-time detection capabilities on edge devices as-sociated with traditional manual visual inspections and subjective sampling methods,this paper pro-poses a lightweight detection model for track fastener status,FTEL-YOLO,based on YOLOv8s.The model is designed to enhance detection accuracy and real-time performance.First,the C2f-Faster module,inspired by the FasterNet-Block concept,is introduced to reduce the model's parameters.Second,to mitigate the decline in detection accuracy caused by network lightweighting,a Triplet At-tention Mechanism is incorporated after the Spatial Pyramid Pooling Fast(SPPF)layer,and EIoU is utilized as the bounding box regression loss function,enhancing the model's feature extraction capabil-ity for track fastener conditions in complex backgrounds.Finally,Layer-Adaptive Magnitude-based Pruning(LAMP)is applied to the improved model to further compress it,reducing redundancy and en-hancing its deployment capability on edge devices.Experimental results demonstrate that the improved FTEL-YOLO model achieves a minimal detection accuracy loss of 0.3%,while the computation,pa-rameters,and model size are reduced by 63.1%,65.6%,and 66.2%,respectively,achieving light-weight design without compromising accuracy.
deep learningfault detectiontrack fastenersYOLOv8striplet attentionmodel light-weighting
万方数据
维普
