Defects Recognition Method of Pantograph Contact Strip Based on Improved Convolutional Neural Network
The pantograph contact strip monitoring device(5C)uses high-definition imaging devices to obtain images of the locomotive's pantograph contact strip to ensure that the pantograph is in good operating condition.Aiming at the problem of low detection accuracy in traditional methods,a method for identifying pantograph contact strip plate defects based on convolutional neural networks is proposed.Replacing the YOLO v5 network activation function improves the training speed and generalization ability of the model,balances the proportion of positive and negative samples through data enhancement methods.On-site 5C experiments data show that the accuracy rate of this method reaches 97.33%,the recall rate reaches 88.77%,and the F1 score reaches 92.85%,demonstrating the application value in actual railway scenarios.
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