Infrared Image Defect Detection Based on the Algorithm of Intuitionistic Fuzzy C-Means Clustering

Infrared thermal imaging technology is often used to detect internal defects in carbon-fiber-reinforced polymers; however, commonly used optical heat sources are inefficient and require the specimen to be heated over a short distance. Lasers offer the advantages of energy concentration and low-energy attenuation. As a heating source, it helps achieve long-distance nondestructive testing (NDT). This paper introduces the technology of line laser scanning infrared thermal imaging and analyzes the thermal transmission process inside the material during the heating process. Second, the uniformity and contrast of the infrared image are poor, which is not conducive to defect-feature extraction. Here, image segmentation methods were based on the intuitionistic fuzzy C-means (IFCM) clustering algorithm to extract defect edges. Compared with the K-means clustering method, this method can improve the recognition and detection of fuzzy edges of defects, retain more detailed information of images, and help extract the features of the defect edges accurately.