Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically require large annotated datasets for supervised training. Once a crack is detected, monitoring its severity often demands precise segmentation of the damage. However, pixel-level annotation of images for segmentation is labor-intensive. To mitigate this cost, one can leverage explainable artificial intelligence (XAI) to derive segmentations from the explanations of a classifier, requiring only weak image-level supervision. This paper proposes applying this methodology to segment and monitor surface cracks. We evaluate the performance of various XAI methods and examine how this approach facilitates severity quantification and growth monitoring. Results reveal that while the resulting segmentation masks may exhibit lower quality than those produced by supervised methods, they remain meaningful and enable severity monitoring, thus reducing substantial labeling costs.

翻译：监测基础设施的表面裂纹对于结构健康监测至关重要。自动视觉检测提供了一种有效的解决方案，尤其适用于难以触及的区域。机器学习方法已证明其有效性，但通常需要大量标注数据集进行监督训练。一旦检测到裂纹，监测其严重程度通常需要精确的损伤分割。然而，为分割任务进行的像素级图像标注劳动强度大。为降低这一成本，可利用可解释人工智能（XAI）从分类器的解释中推导出分割结果，仅需弱图像级监督。本文提出应用该方法对表面裂纹进行分割与监测。我们评估了多种XAI方法的性能，并研究了该方法如何促进严重程度量化与扩展监测。结果表明，尽管生成的分割掩模质量可能低于监督方法，但其仍具实际意义，能够实现严重程度监测，从而大幅降低标注成本。