Non-destructive detection of components using x-ray based on unsupervised learning

As a non-destructive detection method, X-rays are widely used in the field of electronic component inspection. However, the subsequent defect detection needs to be completed manually, which leads to poor efficiency and low reliability due to a large number of components. To solve the above problems, we propose a component X-ray image defect detection method based on deep learning. On the one hand, we have designed an algorithm for the segmentation and correction of X-ray images. On the other hand, in the case of fewer defect samples and variable defect forms, we only use defect-free samples for training. We propose an unsupervised learning model based on variational autoencoder and add a convolutional attention module to realize automatic reconstruction of defects. In addition, we combine gradient magnitude similarity and absolute error of the input image and the reconstructed image to detect and locate the defect region. The effectiveness of the proposed method is verified through experiments on a typical X-ray dataset, and the accuracy of defects detection reaches about 99%.