发表机构
Digital Factory Vorarlberg GmbH; illwerke vkw AG; University of Salzburg; Vorarlberg University of Applied Sciences（福拉尔贝格数字工厂有限公司; 伊尔沃克VKW股份公司; 萨尔茨堡大学; 福拉尔贝格应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种无需缺陷参考即可在真实图像上合成分级缺陷的框架，用于数据稀缺下的模型选择，实验表明可显著降低选择遗憾并有效验证检测模型。
AI 中文摘要
工业状态监测中的缺陷检测系统只有在经过验证后才能被信赖，然而缺陷样本很少，并且对于特定资产而言，往往根本不存在。我们提出一个框架，在没有任何目标资产缺陷参考的情况下，在真实的非缺陷图像上合成具有严重程度分级的缺陷，该框架可用于模型选择和验证。从文献中提炼出的常见故障模式缺陷分类法被转化为不同缺陷严重程度的规范性提示。从无缺陷图像中裁剪出感兴趣区域，并使用预训练图像生成模型（“FLUX.2 [klein]”）进行编辑。采用颜色匹配和混合来改善与原始图像的结构连贯性。生成的图像通过评分器和估计的检测难度进行过滤。在MVTecAD上的模型选择实验表明，与在访问测试数据情况下选择的最佳固定模型相比，图像AUROC的模型选择遗憾值几乎减半。实验表明需要严重程度分级的异常合成。一项案例研究调查了所提方法在水电中佩尔顿水轮机转轮现场监测中的应用，其中真实缺陷图像稀少且采集成本高昂。基于PatchCore的异常检测模型在佩尔顿水轮机图像上拟合，并使用合成图像进行选择和验证，显示出强大的检测性能（在最佳阈值下正确检测率为94%，AUROC为0.97）。该模型可靠地检测中度和重度缺陷，而早期缺陷仍然具有挑战性，表明合成数据有意义地考验了检测器的灵敏度。
英文摘要
Defect detection systems for industrial condition monitoring can only be relied upon if they are validated, yet defective samples are rare and, for a specific asset, often nonexistent. We present a framework that synthesizes severity-graded defects on real non-defective images without any defect references for the target asset, that can be used for model selection and validation. A defect taxonomy for common failure modes is distilled from literature into prescriptive prompts at varying defect severities. Regions of interest are cropped from in defect-free images and edited with a pre-trained image generation model ("FLUX.2 [klein]"). Color-matching and blending are employed to improve structural coherence with the original image. Generations are filtered out by a scorer and by estimated detection difficulty. Model selection experiments on MVTecAD show image AUROC choice regret over model selection can be nearly halved compared to the best fixed model chosen with access to test data. Experiments show the need for severity-graded anomaly synthesis. A case study investigates the proposed method for in-situ monitoring of Pelton turbine runners in hydropower, where real defect images are rare and expensive to collect. A PatchCorebased anomaly detection model is fit on Pelton turbine images and selected and validated using synthetic images, showing strong detection performance (94 % correct detection at optimal threshold and AUROC 0.97). The model reliably detects moderate and advanced defects, while early-stage defects remain challenging, indicating the synthetic data meaningfully stresses detector sensitivity.
