Hierarchical low-rank and sparse tensor micro defects decomposition by electromagnetic thermography imaging system

Wu, Tongle, Gao, Bin and Woo, Wai Lok (2020) Hierarchical low-rank and sparse tensor micro defects decomposition by electromagnetic thermography imaging system. Philosophical Transactions of the Royal Society A: Mathematical Physical and Engineering Sciences, 378 (2182). p. 20190584. ISSN 1364-503X

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Official URL: https://doi.org/10.1098/rsta.2019.0584

Abstract

With the advancement of electromagnetic induction thermography and imaging technology in non-destructive testing field, this system has significantly benefitted modern industries in fast and contactless defects detection. However, due to the limitations of front-end hardware experimental equipment and the complicated test pieces, these have brought forth new challenges to the detection process. Making use of the spatio-temporal video data captured by the thermal imaging device and linking it with advanced video processing algorithm to defects detection has become a necessary alternative way to solve these detection challenges. The extremely weak and sparse defect signal is buried in complex background with the presence of strong noise in the real experimental scene has prevented progress to be made in defects detection. In this paper, we propose a novel hierarchical low-rank and sparse tensor decomposition method to mine anomalous patterns in the induction thermography stream for defects detection. The proposed algorithm offers advantages not only in suppressing the interference of strong background and sharpens the visual features of defects, but also overcoming the problems of over- and under-sparseness suffered by similar state-of-the-art algorithms. Real-time natural defect detection experiments have been conducted to verify that the proposed algorithm is more efficient and accurate than existing algorithms in terms of visual presentations and evaluation criteria.

Item Type: Article
Subjects: H600 Electronic and Electrical Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 18 Dec 2020 15:36
Last Modified: 18 Dec 2020 15:45
URI: http://nrl.northumbria.ac.uk/id/eprint/45064

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