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arXiv · 2512.06171

Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection

Jessica Plassmann·Nicolas Schuler·Michael Schuth·Georg von Freymann
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Abstract

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.

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Jessica Plassmann, Nicolas Schuler, Michael Schuth, Georg von Freymann. 2025-12-05. Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection. https://arxiv.org/abs/2512.06171

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