无监督学习在工业缺陷检测中的应用：对剪切图像数据的案例研究
Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data
AI总结：
本研究通过无监督学习方法，针对剪切图像数据实现自动化异常检测，验证了学生-教师模型在缺陷分类和定位上的优越性能。
摘要：
剪切法是一种用于检测 subsurface 缺陷的非破坏性检测方法，具有高灵敏度和全场检查能力。然而，其工业应用仍然有限，因为需要专家解释。为了减少对标记数据和手动评估的依赖，本研究探讨了无监督学习方法，用于自动化剪切图像中的异常检测。评估了三种架构：全连接自动编码器、卷积自动编码器和学生-教师特征匹配模型。所有模型仅在无缺陷数据上进行训练。通过定制样品开发了一个受控数据集，该样品具有可重复的缺陷模式，使能够系统地获取剪切测量数据，在理想和现实变形条件下。定义了两个训练子集：一个只包含未变形、无缺陷的样本，另一个还包含全局变形但无缺陷的数据。后者通过引入由变形引起的条纹图案来模拟实际检测条件，这些图案可能掩盖局部异常。模型在二元分类和学生-教师模型中的空间缺陷定位方面进行评估。结果显示，学生-教师方法在分类鲁棒性和定位方面表现更优。与基于自动编码器的模型相比，它在特征表示的分离度上有所提高，通过t-SNE嵌入图可视化。此外，一个在标记缺陷数据上训练的YOLOv8模型用作参考，以基准定位质量。本研究强调了无监督深度学习在工业环境中可扩展、标签高效剪切检查的潜力。
英文摘要：
Shearography is a non-destructive testing method for detecting subsurface defects, offering high sensitivity and full-field inspection capabilities. However, its industrial adoption remains limited due to the need for expert interpretation. To reduce reliance on labeled data and manual evaluation, this study explores unsupervised learning methods for automated anomaly detection in shearographic images. Three architectures are evaluated: a fully connected autoencoder, a convolutional autoencoder, and a student-teacher feature matching model. All models are trained solely on defect-free data. A controlled dataset was developed using a custom specimen with reproducible defect patterns, enabling systematic acquisition of shearographic measurements under both ideal and realistic deformation conditions. Two training subsets were defined: one containing only undistorted, defect-free samples, and one additionally including globally deformed, yet defect-free, data. The latter simulates practical inspection conditions by incorporating deformation-induced fringe patterns that may obscure localized anomalies. The models are evaluated in terms of binary classification and, for the student-teacher model, spatial defect localization. Results show that the student-teacher approach achieves superior classification robustness and enables precise localization. Compared to the autoencoder-based models, it demonstrates improved separability of feature representations, as visualized through t-SNE embeddings. Additionally, a YOLOv8 model trained on labeled defect data serves as a reference to benchmark localization quality. This study underscores the potential of unsupervised deep learning for scalable, label-efficient shearographic inspection in industrial environments.
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