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Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification

Wilson, Ben A.; Ledger, Paul D.; Lionheart, William R. B.

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Authors

Ben A. Wilson

William R. B. Lionheart



Abstract

The early detection of terrorist threat objects, such as guns and knives, through improved metal detection, has the potential to reduce the number of attacks and improve public safety and security. To achieve this, there is considerable potential to use the fields applied and measured by a metal detector to discriminate between different shapes and different metals since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characterisation of metallic objects and its spectral signature provides additional object characterisation information. The MPT spectral signature can be determined from measurements of the induced voltage over a range frequencies in a metal signature for a hidden object. With classification in mind, it can also be computed in advance for different threat and non-threat objects. In the article, we evaluate the performance of probabilistic and non-probabilistic machine learning algorithms, trained using a dictionary of computed MPT spectral signatures, to classify objects for metal detection. We discuss the importances of using appropriate features and selecting an appropriate algorithm depending on the classification problem being solved and we present numerical results for a range of practically motivated metal detection classification problems.

Citation

Wilson, B. A., Ledger, P. D., & Lionheart, W. R. B. (2022). Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification. International Journal for Numerical Methods in Engineering, 123(9), 2076-2111. https://doi.org/10.1002/nme.6927

Journal Article Type Article
Acceptance Date Jan 10, 2022
Online Publication Date Jan 17, 2022
Publication Date May 15, 2022
Publicly Available Date May 30, 2023
Journal International Journal for Numerical Methods in Engineering
Print ISSN 0029-5981
Publisher Wiley
Volume 123
Issue 9
Pages 2076-2111
DOI https://doi.org/10.1002/nme.6927
Keywords Applied Mathematics, General Engineering, Numerical Analysis
Publisher URL https://onlinelibrary.wiley.com/doi/10.1002/nme.6927

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