基于单尺度Retinex与改进的K-均值聚类的涡流热成像缺陷检测

Defect Detection of Eddy-Current Thermography Based on Single-Scale Retinex and Improved K-means Clustering

在利用涡流红外热成像技术检测金属材料损伤缺陷时,因热波属于衰减波,且热波三维热扩散等问题,导致采集的红外图像中缺陷部位模糊。针对该问题,提出一种基于单尺度Retinex与改进的K-均值聚类的缺陷检测方法,用于处理红外图像特征增强、图像分割和边缘特征提取等问题。该方法首先利用单尺度Retinex(single-scale Retinex,SSR)对红外热图像进行图像增强,强化缺陷特征,然后利用改进的K-均值聚类算法对图像进行分割,最后采用数学形态学算法处理图像,去除缺陷图像中无用信息,并利用Canny算子检测出缺陷边缘。实验结果证明,该方法有效地检测出金属材料试件缺陷,并提取出完整清晰的缺陷边缘。

When eddy-current infrared thermal-imaging technology is used to detect metal-material damage defects,the infrared image is susceptible to noise and may also contain useless information,which can result in blurring of damage defects.To address this problem,a defect-detection method based on single-scale Retinex and improved K-means clustering is proposed to perform infrared image-feature enhancement,image segmentation,and edge feature extraction.First,the image is enhanced using single-scale Retinex.Additionally,the defect features are enhanced.Then,an improved K-means clustering algorithm is used to segment the image.Finally,a mathematical morphology algorithm is used to process the image,which removes the useless information in the defective image and uses a Canny operator to detect the defect edge.The experimental results show that the method effectively detects defects of metal-material specimens and extracts complete and clear defect edges of the metal-material specimens.