A study on unsupervised anomaly detection and defect localization using generative model in ultrasonic non-destructive testing

Abstract

In recent years, the importance of non-destructive testing has increased due to the aging of civil engineering structures and nuclear equipment. Ultrasonic non-destructive testing is one of the most widely used non-destructive testing methods and is used extensively from the manufacturing processes of various materials to the on-site inspections. Recently, advanced ultrasonic measurement techniques, such as Laser Ultrasonic Visualization Testing (LUVT), have been developed, and attempts to use machine learning for automatic inspection are also being explored. However, the lack of anomalous data with defects poses a barrier to improving the accuracy of automated inspection through machine learning. Therefore, in this study, we propose a method for automated LUVT inspection using an anomaly detection approach with a diffusion model that can be trained solely on negative examples (defect-free data). We experimentally confirmed that our proposed method improves defect detection and localization compared to general object detection algorithms used previously.