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Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics

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Abstract

With the increasing use of induction thermography (IT) for nondestructive testing in the mechanical and rail industry, it becomes necessary for the Manufacturers to rapidly and accurately monitor the health of specimens. The most general problem for IT detection is due to strong noise interference. In order to counter it, general postprocessing is carried out. However, due to the more complex nature of noise and irregular shape specimens, this task becomes difficult and challenging. In this article, a low-rank tensor with a sparse mixture of Gaussian (LRTSMoG) decomposition algorithm for natural crack detection is proposed. The proposed algorithm models jointly the LRST pattern by using a tensor decomposition framework. In particular, the weak natural crack information can be extracted from strong noise. Low-rank tensor based iterative sparse MoG noise modeling is carried out to enhance the weak natural crack information as well as reducing the computational cost. In order to show the robustness and efficacy of the model, experiments are conducted for natural crack detection on a variety of specimens. A comparative analysis is presented with general tensor decomposition algorithms. The algorithms are evaluated quantitatively based on signal-to-noise-ratio along with the visual comparative analysis.

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Ahmed, J., Gao, B., & Woo, W. L. (2021). Sparse Low-Rank Tensor Decomposition for Metal Defect Detection Using Thermographic Imaging Diagnostics. IEEE Transactions on Industrial Informatics, 17(3), 1810–1820. https://doi.org/10.1109/TII.2020.2994227

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