Wilson, BA, Ledger, P ORCID: https://orcid.org/0000-0002-2587-7023 and Lionheart, WRB (2022) Identification of Metallic Objects using Spectral Magnetic Polarizability Tensor Signatures: Object Classification. International Journal for Numerical Methods in Engineering, 123 (9). pp. 2076-2111.

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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.

Item Type: Article
Additional Information: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.© 2022 The Authors. International Journal for Numerical Methods in Engineering published by John Wiley & Sons Ltd
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HT Communities. Classes. Races
H Social Sciences > HV Social pathology. Social and public welfare
H Social Sciences > HV Social pathology. Social and public welfare > HV6431 Terrorism
T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 14 Jan 2022 13:14
Last Modified: 08 Jun 2022 13:35
URI: https://eprints.keele.ac.uk/id/eprint/10500

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