A Novel Defect Segmentation for Pulse Infrared Images Based on Improved Fuzzy C-Means Algorithm With Weighted Distance

Abstract

Pulsed infrared nondestructive testing (NDT) technology can effectively identify defects in engineering materials or structures. However, the original infrared images have the problems of low signal-to-noise ratio (SNR) and blurred edges. A novel defect segmentation method for pulsed infrared images based on improved fuzzy C-means algorithm with weighted distance is proposed. First, the original infrared images are denoised by wavelet transform. Second, an improved fuzzy C-means algorithm based on weighted distance (WDFCM) is proposed to cluster the infrared images. Afterward, the maximum between-cluster variance method (OTSU) is used to automatically select the thresholds and the binarization is performed to extract defect features. The Sobel edge detection operator is used to completely extract the structural properties of the defect regions. Finally, an experimental platform for pulsed infrared thermal imaging was built with two kinds of materials: flexible pipes and steel structures. The method proposed in this article was used for image segmentation subsequently. The results verify that the performance of the proposed method is optimal. The accuracy of defect detection can reach 98.95%, which is 16.82% higher than the original method.