Research on Classification and Recognition of Industrial Stainless Steel Welded Pipe Defects Based on Convolution Neural Network

Abstract

For the classification and identification of industrial stainless steel welded pipe defects, a combined method of STFT and CNN based on vortex test is proposed. First, the collected original eddy current signal is STFT transformed to obtain a two-dimensional time-frequency map. Then, the two-dimensional time-frequency map was input into the two neural networks VGG-16 and GoogLeNet for model training, selecting a more accurate network model under the condition of the same learning rate. The trained network model is then classified using different learning rates. The results show that with the learning rate of 0.0001, the VGG-16 training model is better than the GoogLeNet training model, which has a certain reference significance for the classification and identification of defects in industrial stainless steel welded pipes.