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.

本研究提出了一种针对高噪声热红外图像的缺陷分割新方法 DefectSAM，通过采用广泛采用的模型 Segment Anything（SAM）和精心策划的数据集，有效地提高了缺陷检测率，尤其在复杂和不规则表面上对弱缺陷和小缺陷的检测表现出色，减少了遗漏检测的发生，并提供了更准确的缺陷尺寸估计。实验研究验证了我们解决方案在各种材料上缺陷检测的有效性，具有加速缺陷检测工具演进、提高检测能力和精度的重要潜力。