Intelligent Detection Algorithm Based on 2D/3D-UNet for Internal Defects of Carbon Fiber Composites

• Published in: Nondestructive testing and evaluation 2024-05, Vol.39 (4), p.923-938
• Main Authors: He, Yunze, Mu, Xinying, Wu, Jiarong, Ma, Yue, Yang, Ruizhen, Zhang, Hong, Wang, Pan, Wang, Hongjin, Wang, Yaonan
• Format: Article
• Language: English
• Subjects: Algorithms; Background noise; Carbon fibers; Composite materials; Convolution; Deep learning; defect; Defects; Fast Fourier transformations; Fiber composites; Fourier transforms; Image reconstruction; Infrared imaging; infrared thermography; machine vision; Modules; Principal components analysis; Semantic segmentation; Signal reconstruction; Temperature distribution; Thermal imaging; Thermography
• ISSN: 1058-9759; 1477-2671
• DOI: 10.1080/10589759.2023.2234548

Infrared thermography testing (IRT) has been widely used in the defect detection of composite materials. However, the identification of defects characteristics is unsatisfying due to the interference of factors such as uneven background and noise in the original thermal image sequence. A novel thermography-based defect detection method with the semantic segmentation network is proposed to enhance the defect contrast and extract perfect features. To Figure out the abnormal distribution of temperature field in thermal images, AG-UNet was used with a spatial self-attention gate module to extract spatial features of thermal images. The 3D-UNet network was obtained by the 3D convolution module and the temporal convolution module to extract thermal temporal and spatial features simultaneously which could help the defect segmentation in thermal videos. Compared with traditional algorithms such as principal component analysis (PCA), thermographic signal reconstruction (TSR), and fast Fourier transform (FFT), defects detection results were significantly enhanced with the proposed method, and defects of smaller diameter-to-depth ratio can be detected by deep learning models.