Abstract
Nowadays, Automatic metal surface defect recognition is an important research direction in the field of surface defect recognition, and more convolution neural network algorithms are applied in this field. However, with the deepening of network layers, network degradation will occur. We propose a ResNet method for classifying metal surface defects. After experimental testing, we use ResNet34 to build an identification network. After training 300 epoch of the network using the NEU surface defect data set, the convergence of the network is very good. The accuracy of test set verification is 93.67% and higher than that of other surface defect recognition algorithms. Also, we can deepen the number of layers ResNet the network without worrying about network degradation.
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