Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification Article Swipe
Ben Wilson
,
Paul D. Ledger
,
William Lionheart
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/nme.6927
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/nme.6927
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 characterization information. The magnetic polarizability tensor (MPT) offers an economical characterization of metallic objects and its spectral signature provides additional object characterization information. The MPT spectral signature can be determined from measurements of the induced voltage over a range of 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 this 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 importance 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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/nme.6927
- OA Status
- hybrid
- Cited By
- 18
- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W3206320390
All OpenAlex metadata
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https://openalex.org/W3206320390Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/nme.6927Digital Object Identifier
- Title
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Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classificationWork title
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articleOpenAlex work type
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enPrimary language
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2022Year of publication
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2022-01-17Full publication date if available
- Authors
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Ben Wilson, Paul D. Ledger, William LionheartList of authors in order
- Landing page
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https://doi.org/10.1002/nme.6927Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1002/nme.6927Direct OA link when available
- Concepts
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Probabilistic logic, Polarizability, Artificial intelligence, Computer science, Pattern recognition (psychology), Signature (topology), Detector, Characterization (materials science), Physics, Mathematics, Optics, Quantum mechanics, Molecule, Telecommunications, GeometryTop concepts (fields/topics) attached by OpenAlex
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18Total citation count in OpenAlex
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2025: 2, 2024: 4, 2023: 7, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3148949726, https://openalex.org/W6980536890, https://openalex.org/W2795016821, https://openalex.org/W2269930028, https://openalex.org/W2963949442, https://openalex.org/W2969752174, https://openalex.org/W2051799546, https://openalex.org/W2024506048, https://openalex.org/W3168656018, https://openalex.org/W4205462731, https://openalex.org/W2063424476, https://openalex.org/W2988347521, https://openalex.org/W1974124603, https://openalex.org/W2782000094, https://openalex.org/W2327245997, https://openalex.org/W2312571836, https://openalex.org/W2085991117, https://openalex.org/W2140289357, https://openalex.org/W2769875332, https://openalex.org/W1735387029, https://openalex.org/W3112360490, https://openalex.org/W4212863985, https://openalex.org/W2048641376, https://openalex.org/W2142873169, https://openalex.org/W429766147, https://openalex.org/W2787894218, https://openalex.org/W1563088657, https://openalex.org/W1980501707, https://openalex.org/W1678356000, https://openalex.org/W2135695572, https://openalex.org/W2093040750, https://openalex.org/W2053154970, https://openalex.org/W1974024597, https://openalex.org/W2131241448, https://openalex.org/W2942621001, https://openalex.org/W1663973292, https://openalex.org/W3098467005, https://openalex.org/W1867448996, https://openalex.org/W2884745705, https://openalex.org/W46659105, https://openalex.org/W1480376833, https://openalex.org/W4213009331, https://openalex.org/W1532325895, https://openalex.org/W3213963107, https://openalex.org/W2167443288 |
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| abstract_inverted_index.Abstract | 0 |
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| abstract_inverted_index.spectral | 82, 91, 153 |
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| corresponding_author_ids | https://openalex.org/A5042953146 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I56007636 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[1].score | 0.4399999976158142 |
| sustainable_development_goals[1].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.82658749 |
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| citation_normalized_percentile.is_in_top_10_percent | False |