Research On Defect Detection Algorithm Of Train Parts Based On Machine Vision

In recent years,due to the further promotion of urbanization,urban rail transit has developed unprecedentedly in order to ensure the travel needs of urban residents.As a result,the subway train load is further increased,and the parts of the train running part are more prone to hidden dangers such as parts breakage and loss.Therefore,in order to ensure the travel safety of the people,it is necessary to put forward higher requirements for the safety inspection of trains.The traditional subway train inspection is mainly manual,which has the characteristics of high cost and low efficiency.Based on the high-definition linear array images of the side of the subway train running part collected at the inspection site,this paper studies a series of algorithms for the defect detection process of train parts based on machine vision,and adopts the methods of data expansion,part positioning and defect detection based on deep learning to overcome the problems of small sample size,unbalanced positive and negative samples and low detection accuracy,It provides a reliable solution for the automatic inspection of subway trains.The main contents include:1)This paper analyzes the research status of defect detection,image data expansion and one-class classification of key train components at home and abroad,and leads to the technical scheme for the above difficulties in this paper.It further introduces the principle of convolutional neural network,classical feature extraction network and one-class classification network,which provides a theoretical basis and technical way for the later paper.2)Using SRGAN network,aiming at the characteristics of small sample size of clamp item points and inconsistent picture size and uneven distribution of clamp item points,the data expansion of clamp item points is realized based on super-resolution image reconstruction.3)Using YOLOv5 network,the key parts of clamp item and electric box cover item are located.In order to ensure that the technical scheme can be better applied to the embedded equipment in the future work,the structure of YOLOv5 network is improved,ordinary convolution is replaced by Ghost Conv,the CSPbottleneck layer is replaced by Ghostbottleneck,and an attention mechanism is added.After testing,the m AP of the improved YOLOv5 reaches 0.991,While keeping the accuracy unchanged,the detection time of a single picture on the CPU is reduced by 12.90%.4)In view of the characteristics of less negative samples and unbalanced number of positive and negative samples of spring parts in clamp item points,the Differ Net algorithm is used to detect the defects of spring parts in clamp item points,so that the AUC index of spring parts reaches 0.9945.At the same time,using the method of transfer learning,Dense Net121 network is applied to the defect detection steps of rectangle,triangle identification and lock in the item points of the electrical box cover,which ensures that the accuracy is close to 100%when the model is small.In the final joint commissioning experiment,the false alarm rate of the two items is less than 10%,and the omission rate is 0.