Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities. However, challenges such as low resolution, high noise, and uneven heating in infrared thermal images hinder comprehensive and accurate defect detection. In this study, we propose DefectSAM, a novel approach for segmenting defects on highly noisy thermal images based on the widely adopted model, Segment Anything (SAM)\cite{kirillov2023segany}. Harnessing the power of a meticulously curated dataset generated through labor-intensive lab experiments and valuable prompts from experienced experts, DefectSAM surpasses existing state-of-the-art segmentation algorithms and achieves significant improvements in defect detection rates. Notably, DefectSAM excels in detecting weaker and smaller defects on complex and irregular surfaces, reducing the occurrence of missed detections and providing more accurate defect size estimations. Experimental studies conducted on various materials have validated the effectiveness of our solutions in defect detection, which hold significant potential to expedite the evolution of defect detection tools, enabling enhanced inspection capabilities and accuracy in defect identification.

翻译：缺陷检测在红外非破坏性检测系统中具有关键作用，能够实现非接触式、安全且高效的检测能力。然而，红外热像图存在的低分辨率、高噪声和加热不均等问题严重制约了全面精准的缺陷检测。本研究提出DefectSAM——一种基于广泛应用的"任意分割"模型（Segment Anything，SAM）\cite{kirillov2023segany}的创新方法，专用于高噪声热像图中的缺陷分割。通过充分利用实验室艰苦实验生成的精细标注数据集，以及资深专家提供的宝贵提示信息，DefectSAM超越了现有最先进的分割算法，在缺陷检测率上实现显著提升。值得注意的是，该模型在复杂不规则表面上的微弱细小缺陷检测中表现尤为突出，有效降低了漏检率，并能提供更精确的缺陷尺寸估计。基于多种材料的实验研究验证了本方案在缺陷检测中的有效性，其具备显著潜力推动缺陷检测工具的演进，从而增强检测能力与缺陷识别的精确性。