Surface Defect Detection Algorithm for Steel Rails Based on Improved YOLOv8
Steel rails are the main components of railway tracks,and the surface defects of steel rails seriously affect the quality and safety of railway system operation.In response to the low accuracy and efficiency of rail surface damage detection in practical applications,a rail surface damage detection algorithm based on improved YOLOv8 algorithm is proposed.Add SE attention mechanism at the end of the backbone network,establish the interdependence between convolutional feature channels,and improve the network's representation ability.Introducing Spatial Deep Convolution(SPD Conv)to replace the traditional convolution module in YOLOv8 network,by convolving each feature map and preserving all information in the channel dimension,the performance of the model in low resolution image and small object detection is improved.The results indicate that compared with the YOLOv8m model,the improved model mAP@0.5 and mAP@0.5:0.95 They increased by about 6.2%and 10.8%respectively,with a recall rate of about 6.8%and an accuracy rate of about 4.4%,effectively improving detection accuracy and speed.
surface defects of steel railsobject detectionYOLOv8
NETL
NSTL
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