自动标注剪切散斑图像缺陷，降低人工成本。
Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection
- 用Grounded DINO生成候选框，SAM优化掩码。
- 生成的标注可用于弱监督学习，效果可靠。
- 适合工业缺陷检测数据集快速构建。
剪切散斑是一种对表面位移梯度敏感的干涉技术，可高灵敏度检测关键部件中的亚表面缺陷。其工业化应用的主要瓶颈在于缺乏高质量标注数据，因人工标注耗时、主观且难标准化。本文提出自动化标注流程：先用Grounded DINO生成候选缺陷边界框，再通过SAM掩码进行精细化修正，并导出YOLO格式标签用于下游检测器训练。定量评估表明，生成的边界框适用于弱监督学习；高分辨率掩码则提供定性可视化支持。该方法显著减少人工工作量，推动可扩展的工业缺陷检测数据集构建。
原文摘要 · Abstract (English)
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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