Walk-through metal detectors are used at check points for preventing personnel and passengers from carrying threatening metallic objects, such as knives and guns into a secure area. These systems are capable of detecting small metallic items, such as handcuff keys and blades, but are unable to distinguish accurately between threatening objects and innocuous items. This paper studies the extent to which a K-Nearest Neighbour-classifier can distinguish various kinds of metallic objects, such as knives, shoe shanks, belts and containers. The classifier uses features extracted from the magnetic polarisability tensor, which represents the electromagnetic properties of the object. The tests include distinguishing threatening objects from innocuous ones, classifying a set of objects into 13 classes, and distinguishing between several similar objects within an object class. A walk-through metal detection system is used as source for the test data, which consists of 835 scans and 67 objects. The results presented show a typical success rate of over 95% for recognizing threats, and over 85% for correct classification. In addition, we have shown that the system is capable of distinguishing between similar objects reliably. Overall, the method shows promise for the field of security screening and suggests the need for further research.

A tomographic metal detection and characterisation system has been designed and built for recovering information about magnetic and/or conductive objects within the detector space. This information is gathered as a result of a "walk-through" scan of a candidate in the same manner as for a typical security metal detector archway. Following the passage of the candidate, the system uses measurements from an array of coils to calculate the polarisability tensor, which describes the low frequency electromagnetic characteristic of a small metallic object when it interacts with an AC magnetic field. In addition to the magnetic polarisability dyadic tensor the position of the perturbation is also determined as a product of the inversion algorithm. The system has been tested and is capable of inverting object tensors with <20% typical parameter variation, and determines three-dimensional object location with a typical error of less than ±3 cm. In this paper results are shown from a set of four different test object examples, each with a different magnetic polarisability tensor. This object set consists of a ferrite sphere, a ferrite rod, and phantom aluminium and steel handgun shapes.

A previously reported tomographic metal detector which is capable of inverting the location and magnetic polarizability tensor for a single object has been modified such that it is capable of inverting the location and magnetic polarizability tensor for multiple objects. In this paper, the term ‘multiple objects’ refers to up to three independent metallic objects. The results from this paper show that the algorithm works well for objects vertically separated by greater than 40 cm, however the reliability varies as objects are brought closer together, or are at the same vertical height; the estimation of position for multiple objects tends to perform well, but the estimation of the magnetic polarizability tensor becomes poorer. Interactions taking place between objects is presented as one possible explanation for this.

In this paper, a walk-through metal detection (WTMD) portal is used for classification of metallic objects. The classification is based on the inversion of the magnetic polarisability tensor (tensor) of the object. The nature of bias and noise components in the tensor are examined by using real walk-through data, and consequently, a novel classifier is introduced. Furthermore, a novel method for detecting poorly inverted tensors is presented, enabling self-diagnostics for the WTMD portal. Based on the results, the novel methods increase the accuracy of metal object classification and have the potential to improve the reliability of a WTMD system.

No abstract