  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.
