  In recent years, the deterioration of artificial materials used in structures
has become a serious social issue, increasing the importance of inspections.
Non-destructive testing is gaining increased demand due to its capability to
inspect for defects and deterioration in structures while preserving their
functionality. Among these, Laser Ultrasonic Visualization Testing (LUVT)
stands out because it allows the visualization of ultrasonic propagation. This
makes it visually straightforward to detect defects, thereby enhancing
inspection efficiency. With the increasing number of the deterioration
structures, challenges such as a shortage of inspectors and increased workload
in non-destructive testing have become more apparent. Efforts to address these
challenges include exploring automated inspection using machine learning.
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.
