Shearographic Anomaly Detection Dataset (SADD)

Shearography is an emerging optical method for non-destructive testing and is gaining increasing attention in industrial inspection scenarios. However, the development and systematic comparison of machine learning methods to allow higher degrees of shearographic inspection is limited by the lack of publicly available, well-characterized datasets. This paper introduces a curated shearography dataset designed specifically for machine vision research. The dataset comprises systematically designed defect geometries with controlled sizes and orientations, complemented by defect-free samples and annotated measurement artefacts that frequently occur in practical measurements. All annotations are performed by domain experts and are supported by a detailed description of the underlying deformation physics, which explain characteristic shearographic signatures and class ambiguities. This physical context motivates the experimental design and supports informed interpretation of learning-based results beyond purely statistical correlations. The dataset enables learning-based methods across unsupervised, supervised, and zero-shot paradigms, demonstrated through three representative use cases: defect detection, multi-class classification, and text-based automated labeling of shearographic measurements. It provides a standardized and reproducible benchmark for systematic machine vision research, supporting the application of foundation models and other advanced methods in industry-specific inspection scenarios. All data and code are publicly available.