Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910160048357376 |
|---|---|
| author | Plassmann, Jessica Schuler, Nicolas Schuth, Michael von Freymann, Georg |
| author_facet | Plassmann, Jessica Schuler, Nicolas Schuth, Michael von Freymann, Georg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06171 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection Plassmann, Jessica Schuler, Nicolas Schuth, Michael von Freymann, Georg Computer Vision and Pattern Recognition 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. |
| title | Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.06171 |