Identification of Metallic Objects using Spectral Magnetic Polarizability Tensor Signatures: Object Classification
This work addresses the need for improved metal detection to enhance public safety by discriminating threat objects, though it appears incremental as it applies existing ML methods to a new dataset of MPT signatures.
The paper tackles the problem of classifying metallic objects like guns and knives for security screening by using spectral magnetic polarizability tensor (MPT) signatures, evaluating machine learning algorithms trained on computed MPT data to achieve classification in metal detection scenarios.
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.