Detection Method of Rail Screw Hole Damage Based on Improved YOLOv7
A modified algorithm model based on YOLOv7 network was proposed to address the issues of low detection accuracy,long detection time,large parameter count,and high computational resource consumption in the current process of identifying steel rail screw hole damage.By establishing a B-display image dataset and statistically analyzing the area size,aspect ratio,and proportion of the target to be detected in the screw hole B-display image,the YOLOv7 network was improved,including improving the target detection layer,introducing lightweight convolution,increasing coordinate attention mechanism,optimizing loss function,reducing the parameter and computational complexity of the improved algorithm,and improving the network's recognition ability and detection speed.To evaluate the effectiveness of the improved method,ablation experiments were conducted and compared with Faster R-CNN(Region based Convolutional Neural Networks),YOLOv3,and YOLOv5 algorithms.The results show that the proposed improved YOLOv7 algorithm performs better than other algorithms in terms of overall performance,with higher mean average accuracy and smaller false detection rate.The final algorithm has a mean average accuracy of 97%,a parameter size of 18.0×106,a computational complexity of 58.1×109,and a detection time of 7.4 ms.It can be well applied to the detection of steel rail screw hole damage scenarios.
high speed railwayrail damage detectionobject detectionscrew hole damageYOLOv7B-display image
万方数据
维普
