Shearography is an interferometric technique sensitive to surface displacement gradients, providing high sensitivity for detecting subsurface defects in safety-critical components. A key limitation to industrial adoption is the lack of high-quality annotated datasets, since manual labeling remains labor-intensive, subjective, and difficult to standardize. We present an automated labeling pipeline that generates candidate defect bounding boxes with Grounded DINO, refines them using SAM masks, and exports YOLO-format labels for downstream detector training. Quantitative evaluation shows the generated boxes are suitable for weakly supervised learning, while high-resolution masks provide qualitative visualization. This approach reduces manual effort and supports scalable dataset creation for robust industrial defect detection.
@article{arxiv.2512.06171,
title = {Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection},
author = {Jessica Plassmann and Nicolas Schuler and Michael Schuth and Georg von Freymann},
journal= {arXiv preprint arXiv:2512.06171},
year = {2026}
}