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 introduce an automated workflow that generates defect annotations from shearography measurements using deep learning, producing high-resolution segmentation and bounding-box labels. Evaluation against expert-labeled data demonstrates sufficient accuracy to enable weakly supervised training, reducing manual effort and supporting scalable dataset creation for robust defect detection.
Abstract (translated)
剪切光度学是一种干涉测量技术，对表面位移梯度敏感，因此能够以高灵敏度检测安全关键部件中的亚表面缺陷。该技术在工业应用中面临的一个主要限制是缺乏高质量的标注数据集，因为手动标注工作既费时又主观，并且难以标准化。我们介绍了一种自动工作流程，利用深度学习技术从剪切光度测量结果生成缺陷标注，能够产出高分辨率分割和边界框标签。通过与专家标注的数据进行对比评估，证明了其准确性足以支持弱监督训练的实施，从而减少手动劳动强度并为稳健的缺陷检测提供可扩展数据集创建的支持。
URL
https://arxiv.org/abs/2512.06171
