Ultrasonic characterization of small defects based on Res-ViT and unsupervised domain adaptation

This paper investigated the application of deep neural networks and domain adaptation for ultrasonic characterization of elliptical defects that are small and inclined. Based on performance evaluation of deep residual network (ResNet) and vision transformer (ViT), we proposed a novel Res-ViT architecture which fuses deep representative features of both models. Furthermore, we developed an unsupervised domain adaptation method to minimize the distance between the source and target domains, which is measured by maximum mean discrepancy. This approach serves to improve the generalizability of the proposed Res-ViT model in noisy environments. Simulation studies were performed at various noise levels to evaluate robustness of different deep neural networks. The proposed Res-ViT model was shown to reduce the characterization uncertainty of various defect parameters, including size, angle, and aspect ratio. Experiments were performed on three elliptical defects which have large orientation angles of 60^∘ relative to the array direction. The proposed method achieved a 61% reduction in the root-mean-square error (RMSE) of defect size compared to a benchmark approach, which is based on principal component analysis and the nearest neighbor method.