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

Abstract

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.