Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection
This addresses the problem of labor-intensive and subjective manual labeling for safety-critical component inspection, though it is incremental as it applies existing deep learning methods to a new domain-specific data type.
The paper tackles the lack of annotated datasets for shearography defect detection by introducing an automated workflow that generates annotations using deep learning, achieving sufficient accuracy to enable weakly supervised training and reduce manual effort.
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.