Defect Classification Method of X-ray Images Based on Improved U-Net

X-ray nondestructive testing means are widely used in the inspection process of internal defects of parts. In practical inspection, defects are generally determined and rated by manual inspection based on X-ray images, which is inefficient and cannot meet the requirements of high-volume automatic inspection. This paper proposes an automatic defect classification model based on an improved U-Net. First, a classifier is added behind the encoder of U-Net. The encoder and classifier are connected in series to form the main branch of the model to complete the classification task. Second, the decoder of U-Net is improved by adding an attention module. The encoder and the improved decoder form the auxiliary branch of the model, which completes the segmentation task. The model proposed in this paper has two advantages. First, for small defects in images, the segmentation-based auxiliary task enables the model to focus on these small targets during the learning process and learn features with more representational power. Second, the introduction of an attention mechanism in the decoder can suppress the interference information, retain the effective location information, focus on the learning of defective regions, adjust the different feature mapping weights, and improve the performance of the model. The method has been validated on the self-built dataset and the dataset collected from X-ray inspection industrial sites and compared with typical image classification methods. The results show that the proposed method in this paper has higher defect recognition accuracy than other classical deep network models and can effectively identify multiple types of defect features while providing assurance and reference for the quality and safety performance of the parts to be inspected.