基于直觉模糊C均值聚类的红外图像缺陷检测

红外热成像技术常被用来检测碳纤维增强复合材料的内部缺陷，但常用的光学热源加热效率低，需要近距离加热试件。激光具有能量集中、衰减小的优点，其作为加热源有助于实现远距离检测。本文介绍了线激光扫描红外热成像无损检测技术，并对加热过程中材料内部热传导进行了分析。其次，针对红外图像均匀性差、对比度弱，不利于缺陷特征提取的问题，本文引入基于直觉模糊C均值聚类算法的图像分割方法来提取缺陷边缘，与K-Means聚类方法相比，该方法可以提升缺陷模糊边缘的识别和检测能力，保留更多图像的细节信息，有助于准确提取缺陷边缘特征。

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.