2020/10/26 by Qi Li, Li, Qi, Dianzi Liu +3
Computer Science · Engineering · #Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Finance #Image and Object Detection Techniques #Image and Video Processing (eess.IV) #Industrial Vision Systems and Defect Detection #J.2 #Machine Learning (cs.LG) #Optical measurement and interference techniques #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.15605
openalex publication_date 2020/10/26 · openalex created_date 2020/11/09 · openalex updated_date 2026/07/28
Data-driven quantitative defect reconstructions using ultrasonic guided waves has recently demonstrated great potential in the area of non-destructive testing. In this paper, we develop an efficient deep learning-based defect reconstruction framework, called NetInv, which recasts the inverse guided wave scattering problem as a data-driven supervised learning progress that realizes a mapping between reflection coefficients in wavenumber domain and defect profiles in the spatial domain. The superiorities of the proposed NetInv over conventional reconstruction methods for defect reconstruction have been demonstrated by several examples. Results show that NetInv has the ability to achieve the higher quality of defect profiles with remarkable efficiency and provides valuable insight into the development of effective data driven structural health monitoring and defect reconstruction using machine learning.