A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4–6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

This paper describes the development of a large-scale multi-coil arrangement capable of characterizing the Magnetic Polarizability Tensor (MPT) of large threat objects such as firearms. The system has been applied to the measurement of a comprehensive range of weapons made available by the National Firearms Centre of the UK. For comparison, a number of non-threat items such as metallic belt buckles, keys and coins have also been characterized. Clear differences in the magnitude and spectroscopic response of the MPT data for different firearm types and non-threat items are presented. The application of unsupervised Machine Learning (ML) algorithms to MPT data of threat and non-threat objects enable a better understanding of target object classification. The presented results are encouraging as they demonstrate the ability of the MPT used in combination with the adopted classification algorithms to robustly discriminate between threat and non-threat objects.

The Magnetic Polarizability Tensor (MPT) is a representative electromagnetic property of a metallic object, which depends on the size, material, shape, and excitation frequency of the object. The MPT can be used to describe the response of metal detector systems and improve target classification performance in applications utilizing electromagnetic induction spectroscopy. However, for target characterization, a library of possible target objects needs to be created which can be used for training machine learning classifiers. To supplement and benchmark our existing library of simulated and measured MPT object characterizations, it is necessary to be able to measure object characterizations accurately and efficiently. This paper describes a novel method utilizing a truncated icosahedron shaped manipulator and procedure to measure MPT characterizations of non-symmetrical, irregular objects. This new method allows the measurement of the MPT of any appropriately sized object. The method also ensures the MPT characterizations are measured quickly and are well posed, without sacrificing accuracy. Performance of the method is validated by comparing experiment results acquired using the new method with experiment results acquired using a slower method for symmetrical objects as well as synthetic results generated using a commercial finite element package and an optimized dedicated open source MPT-Calculator package, which offers high accuracy and considerable computational advantages. Good agreement between the new method and the other three methods is seen. For all objects that have been characterized, MPT loss-peak magnitude and horizontal positions from all described methods are within five percent of each other at worst.

Radar polarimetric imaging for non-destructive testing is a powerful and flexible tool that can be used to enhance the detection of internal structures. In this study, reinforced autoclaved aerated concrete (RAAC) is measured using a polarimetric system in three different acquisition modes—two downward-looking and one sideways-looking configurations, each at a different height. Each acquisition mode is compared and new polarisation states are created using the principle of polarisation synthesis. Images of the internal structures are created using a 3D imaging algorithm, which are used for the analysis. The comparison between acquisition modes demonstrates that using a higher lift-off and polarisation synthesis could offer more flexible operation in the field, allowing the use of handheld detectors and drone-based systems for inaccessible areas. Additionally, the sideways-looking data captured both horizontal and vertical reinforcement and were detected within a single polarisation channel; this configuration also has reduced clutter from the air–concrete boundary, providing a viable option for single polarisation systems.

A reliable and efficient rail track defect detection system is essential for maintaining rail track integrity and avoiding safety hazards and financial losses. Eddy current (EC) testing is a non-destructive technique that can be employed for this purpose. The trade-off between spatial resolution and lift-off should be carefully considered in practical applications to distinguish closely spaced cracks such as those caused by rolling contact fatigue (RCF). A multi-channel eddy current sensor array has been developed to detect defects on rails. Based on the sensor scanning data, defect reconstruction along the rails is achieved using an inverse algorithm that includes both direct and iterative approaches. In experimental evaluations, the EC system with the developed sensor is used to measure defects on a standard test piece of rail with a probe lift-off of 4-6 mm. The reconstruction results clearly reveal cracks at various depths and spacings on the test piece.

A positioning system employing visual fiducial markers (AprilTags) has been implemented for use with handheld mine detection equipment. To be suitable for a battery-powered real-time application, the system has been designed to operate at low power (<100 mW) and frame rates between 30 and 50 fps. The system has been integrated into an experimental dual-mode detector system. Position-indexed metal detector and ground-penetrating radar data from laboratory and field trials are presented. The accuracy and precision of the vision-based system are found to be 1.2 cm and 0.5 cm, respectively.