2021/10/13 by B. A. Wilson, Wilson, B. A., Paul D. Ledger +3 · 1 citation
Earth and Planetary Sciences · Engineering · Materials Science · #35B30 #35R30 #65N21 #65N30 #Electron and X-Ray Spectroscopy Techniques #FOS: Computer and information sciences #FOS: Mathematics #Geophysical and Geoelectrical Methods #Machine Learning (cs.LG) #Non-Destructive Testing Techniques #Numerical Analysis (math.NA)
paper · pdf · doi:10.48550/arxiv.2110.06624
openalex publication_date 2021/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.