In the process of preparation and long-term service, CFRP / Al honeycomb structure is prone to debonding, delamination, ponding and other defects, so it is very important to detect its state by infrared thermal wave nondestructive testing technology. And In the process of collecting the infrared thermal image sequence of CFRP / Al honeycomb structure defects, there is a large background noise, which easily leads to low detection efficiency and poor contrast. In order to improve the defect detection effect, principal component analysis (PCA) algorithm is used to reduce the dimension of defect feature information of the infrared image sequence after background removal, which can effectively filter out the uneven background noise in the infrared image sequence. Combined with multistructure morphology and PCNN hybrid algorithm, the defect area is extracted by image enhancement and image segmentation. The experimental results show that the proposed method can further filter out the uneven background noise of the infrared image, improve the defect area extraction effect, and effectively improve the defect detection rate.