Magnetic flux leakage image classification method for pipeline weld based on optimized convolution kernel.

In order to intelligently classify magnetic flux leakage signals, this study proposes a method of magnetic flux leakage image classification based on sparse self-coding. With inputting with the magnetic flux leakage image of the pipe weld, it extracts features automatically from the Convolutional Neural Network (CNN) rather than the artificial extraction process. The network classification ability can be improved through pre-training of the convolution kernel and introducing the sparse constraints and the image entropy similarity constraint rules. The experiment uses 500 images of magnetic flux leakage signals to classify the girth welds and spiral welds. The accuracy of classification is 95.1%, which is superior to the traditional convolution neural network model. Experimental results show that the improved model has good feature extraction ability and generalization ability.