Surface defect detection of CFRP materials based on infrared thermography and Attention U-Net algorithm

Abstract

ABSTRACT Carbon fiber reinforced polymer (CFRP) materials have been widely used in aerospace and other fields because of their excellent properties such as high temperature resistance and corrosion resistance, so the nondestructive inspection technology for CFRP materials has become a hot research topic. In this paper, we propose a method based on infrared thermography and Attention U-Net algorithm to characterize the defect shape of CFRP material surface. Firstly, the CFRP surface is scanned by a line laser and the trend of its temperature distribution is recorded using an infrared thermography camera. Subsequently, temperature analysis and entropy value of image information are calculated for individual defect image blocks in order to select images with clear defect contours. Next, the Attention U-Net is used to segment the defect in the image blocks, and the defect shape is characterization. By calculating the evaluation indexes of image segmentation, the method in this paper can achieve 99.57% accuracy, 97.06% recall, 96.63% precision, and the processing time for a single image is 0.13s. Finally, the algorithm of this paper is compared with other algorithms to verify the advantages of this research in the task of detecting surface defects in CFRP materials with fast and high accuracy.