High Robust Track Surface Defect Detection Method Based on Computer Vision Technique
Research purposes:In order to solve the uncertainty problem of decision-making caused by the decrease of robustness of the track defect detection system under the interference of actual natural environmental factors,a method to improve the robustness of the detection system is proposed.Firstly,the mutation algorithm is used to generate diversified robust training samples,so that the model can learn to adapt to various environmental disturbances and changes.Then,the target detection model based on YOLOv5 is selected as the track defect detector to meet the requirements of high precision,real-time performance and high accuracy.Research conclusions:(1)Through the mutation generation algorithm,more diverse robust training samples can be generated,so that the model has more samples to learn various disturbances and changes,so as to adapt to various environments.(2)The selected YOLOv5 target detection model as a track defect detector can not only meet the high requirements of track defect detection for detection accuracy and real-time performance,but also achieve good detection results with its excellent generalization ability even if the number of training samples is limited.It fits the difficulty of data acquisition in the actual track detection scene and shows strong applicability.(3)Experiments were carried out on the track inspection data collected on real subway lines.The experimental results show that the robustness of the robust retrained track defect detector on the disturbance data has been significantly improved.The average accuracy rate is increased by 23.35%,the recall rate is increased by 32.75%,the mAP50 is increased by 30.98%,and the mAP50-95 is increased by 19.54%.(4)The research results can be applied to railway,subway and other transportation fields,which is helpful to ensure the safe operation of the line.
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