A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive Testing
近年来，用于建筑结构的人造材料劣化问题已成为一个严重的社会问题，增加了检查的重要性。非破坏性测试由于能够检测到结构中的缺陷和恶化而受到越来越多的需求。在这些方法中，激光超声波可视化测试（LUVT）脱颖而出，因为它允许可视化超声波传播。这使得通过视觉检测缺陷变得直观，从而提高了检查效率。随着劣化结构的增加，缺乏检验员和无损测试工作量增加等问题日益突出。为解决这些问题，包括利用机器学习进行自动检验。然而，缺陷异常数据的存在对通过机器学习提高自动检测的准确性设置了障碍。因此，在本研究中，我们提出了一种使用异常检测方法（扩散模型）进行自动LUVT检测的方法，该方法可以仅基于负面实例（无缺陷数据）进行训练。我们通过实验验证了我们的方法比之前使用的普通物体检测算法提高了缺陷检测和定位。
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.
https://arxiv.org/abs/2405.16580
https://arxiv.org/pdf/2405.16580.pdf