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.

翻译：剪切散斑是一种用于检测亚表面缺陷的无损检测方法，具有高灵敏度和全场检测能力。然而，由于需要专家判读，其在工业中的应用仍受到限制。为减少对标注数据和人工评估的依赖，本研究探索了无监督学习方法在剪切散斑图像中实现自动异常检测的应用。评估了三种架构：全连接自编码器、卷积自编码器以及师生特征匹配模型。所有模型均仅使用无缺陷数据进行训练。通过使用具有可重复缺陷模式的定制试样，开发了一个受控数据集，能够在理想和实际变形条件下系统获取剪切散斑测量数据。定义了两个训练子集：一个仅包含未变形、无缺陷的样本，另一个额外包含全局变形但仍无缺陷的数据。后者通过纳入可能掩盖局部异常的变形诱导条纹图案，模拟了实际检测条件。模型在二分类性能方面进行评估，并对师生模型进行了空间缺陷定位评估。结果表明，师生方法在分类鲁棒性上表现更优，并能实现精确定位。与基于自编码器的模型相比，该方法通过t-SNE嵌入可视化显示出特征表示的可分离性得到改善。此外，使用标注缺陷数据训练的YOLOv8模型作为参考，以基准化定位质量。本研究强调了无监督深度学习在工业环境中实现可扩展、标签高效的剪切散斑检测的潜力。