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

Tongle Wu, Bin Gao*, Wai Lok Woo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
7 Downloads (Pure)


With the advancement of electromagnetic induction thermography and imaging technology in nondestructive 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.
Original languageEnglish
Article number20190584
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2182
Early online date14 Sept 2020
Publication statusPublished - 16 Oct 2020


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