Identification of Metallic Objects using Spectral Magnetic Polarizability Tensor Signatures: Object Classification

TL;DR

This paper explores using spectral magnetic polarizability tensor signatures combined with machine learning algorithms to improve the classification of metallic objects, enhancing security screening and threat detection.

Contribution

It introduces a method to classify metallic objects based on spectral MPT signatures and evaluates machine learning algorithms for effective object discrimination.

Findings

Spectral MPT signatures enable effective object classification.

Machine learning algorithms can distinguish threat from non-threat objects.

Feature selection impacts classification accuracy.

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.