Self-supervised learning-based defect detection for laser ultrasonic non-destructive testing

Abstract

Non-destructive testing is becoming increasingly important for the maintenance and management of aging social infrastructure. In particular, laser ultrasound visualization testing (LUVT) can visualize the propagation of ultrasonic waves. This makes the detection of defects relatively easy. However, due to the increasing demand for inspections and a shortage of qualified inspectors, automated inspection using artificial intelligence is highly desired. Conventional automated inspection methods face challenges in collecting defect data and achieving high detection performance, necessitating new approaches. Therefore, this study proposes an anomaly detection method based on self-supervised learning. First, the proposed algorithm performs pretraining using a two-class classification with normal and pseudo-anomalous data. Then, it uses the obtained feature extractor to detect defects. Furthermore, the proposed method is compared with multiple anomaly detection methods to evaluate its effectiveness.