X-ray image defect recognition method for pipe weld based on improved convolutional neural network

When convolution neural network (CNN) is applied to weld flaw detection image recognition, the target area is small, the local information is redundant, and the hard saturation region of activation function is less than zero, which makes the model sensitive to input change and difficult to train the network parameters. The super pixel segmentation algorithm (SLIC) and the improved ELU activation function are used to construct CNN model for weld flaw detection image defect recognition. First, the ELU activation function is used in the CNN model to generate better robustness to the input noise when the response gradient disappears, At the same time, the SLIC algorithm is used to deal with the pixels of the image, which increases the proportion of the region of interest in the weld flaw detection image, reduces the local redundant information, and improves the feature extraction ability of the model in the training process. Through the extraction of the region of interest of weld flaw detection image and the establishment of the CNN model described in this paper, the results show that the proposed method has better performance than the traditional convolution neural network in feature extraction, training time and recognition accuracy of weld flaw detection image.