Journal
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Volume 378, Issue 2182, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsta.2019.0584
Keywords
inductive thermal video; tensor decomposition; hierarchical sparse; defect defection
Categories
Funding
- National Natural Science Foundation of China [61971093, 61527803, 61960206010]
- Science and Technology Department of Sichuan, China [2019YJ0208, 2018JY0655, 2018GZ0047]
- Fundamental Research Funds for the Central Universities [ZYGX2019J067]
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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. This article is part of the theme issue 'Advanced electromagnetic non-destructive evaluation and smart monitoring'.
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