基于生成模型的无监督异常检测与缺陷定位在超声无损检测中的研究
A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive Testing
AI总结：
本研究提出基于扩散模型的无监督异常检测方法，用于激光超声无损检测中的缺陷定位，仅用无缺陷数据训练，实验证明优于传统目标检测算法。
摘要：
近年来，结构中使用的合成材料劣化已成为一个严重的社会问题，从而增加了检测的重要性。无损检测因其能够在保持结构功能的同时检测缺陷和劣化的能力，需求日益增长。其中，激光超声可视化检测（LUVT）因其能够可视化超声传播而脱颖而出。这使得缺陷检测在视觉上直观明了，从而提高了检测效率。随着劣化结构数量的增加，无损检测中检测人员短缺和工作量增加等挑战变得更加明显。应对这些挑战的努力包括探索使用机器学习的自动化检测。然而，缺乏带有缺陷的异常数据对通过机器学习提高自动化检测准确性构成了障碍。因此，在本研究中，我们提出了一种用于自动化LUVT检测的方法，采用基于扩散模型的异常检测方法，该方法仅需负样本（无缺陷数据）即可训练。我们通过实验证实，与以往使用的通用目标检测算法相比，我们提出的方法在缺陷检测和定位方面有所改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
