Shallow Buried Improvised Explosive Device Detection Via Convolutional Neural Networks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method

Coherence-factor-based rough surface clutter suppression for forward-looking GPR imaging

We present an enhanced imaging procedure for suppression of the rough surface clutter arising in forward-looking ground-penetrating radar (FL-GPR) applications. The procedure is based on a matched filtering formulation of microwave tomographic imaging, and employs coherence factor (CF) for clutter suppression. After tomographic reconstruction, the CF is first applied to generate a "coherence map" of the region in front of the FL-GPR system illuminated by the transmitting antennas. A pixel-by-pixel multiplication of the tomographic image with the coherence map is then performed to generate the clutter-suppressed image. The effectiveness of the CF approach is demonstrated both qualitatively and quantitatively using electromagnetic modeled data of metallic and plastic shallow-buried targets

Modern GPR Target Recognition Methods

Traditional GPR target recognition methods include pre-processing the data by removal of noisy signatures, dewowing (high-pass filtering to remove low-frequency noise), filtering, deconvolution, migration (correction of the effect of survey geometry), and can rely on the simulation of GPR responses. The techniques usually suffer from the loss of information, inability to adapt from prior results, and inefficient performance in the presence of strong clutter and noise. To address these challenges, several advanced processing methods have been developed over the past decade to enhance GPR target recognition. In this chapter, we provide an overview of these modern GPR processing techniques. In particular, we focus on the following methods: adaptive receive processing of range profiles depending on the target environment; adoption of learning-based methods so that the radar utilizes the results from prior measurements; application of methods that exploit the fact that the target scene is sparse in some domain or dictionary; application of advanced classification techniques; and convolutional coding which provides succinct and representatives features of the targets. We describe each of these techniques or their combinations through a representative application of landmine detection.Comment: Book chapter, 56 pages, 17 figures, 12 tables. arXiv admin note: substantial text overlap with arXiv:1806.0459

UAV-mounted Ground Penetrating Radar: an example for the stability analysis of a mountain rock debris slope

This paper describes scientific research conducted to highlight the potential of an integrated GPR-UAV system in engineering-geological applications. The analysis focused on the stability of a natural scree slope in the Germanasca Valley, in the western Italian Alps. As a consequence of its steep shape and the related geological hazard, the study used different remote sensed methodologies such as UAV photogrammetry and geophysics survey by a GPR-drone integrated system. Furthermore, conventional in-situ surveys led to the collection of geological and geomorphological data. The use of the UAV-mounted GPR allowed us to investigate the bedrock depth under the detrital slope deposit, using a non-invasive technique able to conduct surveys on inaccessible areas prone to hazardous conditions for operators. The collected evidence and the results of the analysis highlighted the stability of the slope with Factors of Safety, verified in static conditions (i.e., natural static condition and static condition with snow cover), slightly above the stability limit value of 1. On the contrary, the dynamic loading conditions (i.e., seismic action applied) showed a Factor of Safety below the stability limit value. The UAV-mounted GPR represented an essential contribution to the surveys allowing the definition of the interface debris deposit-bedrock, which are useful to design the slope model and to evaluate the scree slope stability in different conditions

A vision-based terrain morphology estimation model inspired by the avian hippocampus

AbstractHoming pigeons are known for their ability to return home after being released from a location spanning up to hundreds of miles. They make use of detected visual features in the environment, the earth׳s magnetic field as well as using the hippocampus region of the brain to construct spatial maps of the environment. This is unlike present day UAVs that rely on GPS and radio/satellite communications with a ground station, both of which might not be available during a major disaster scenario such as a solar flare.In this paper, we take inspiration from the avian hippocampus and develop a preliminary model for estimating a terrain׳s morphology using visually detected features on the terrain. This could then be used to localise a portable micro-UAV during a demining task for humanitarian purposes in third world countries affected by buried land mines from previous wars. Our goal is that in future, the presented model and algorithm in this work would enable effective coverage of an affected area using the visual information obtained from the environment

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

An evaluation of the performance of multi-static handheld ground penetrating radar using full wave inversion for landmine detection

This thesis presents an empirical study comparing the ability of multi-static and bi-static, handheld, ground penetrating radar (GPR) systems, using full wave inversion (FWI), to determine the properties of buried anti-personnel (AP) landmines. A major problem associated with humanitarian demining is the occurrence of many false positives during clearance operations. Therefore, a reduction of the false alarm rate (FAR) and/or increasing the probability of detection (POD) is a key research and technical objective. Sensor fusion has emerged as a technique that promises to significantly enhance landmine detection. This study considers a handheld, combined metal detector (MD) and GPR device, and quantifies the advantages of the use of antenna arrays. During demining operations with such systems, possible targets are detected using the MD and further categorised using the GPR, possibly excluding false positives. A system using FWI imaging techniques to estimate the subsurface parameters is considered in this work.A previous study of multi-static GPR FWI used simplistic, 2D far-field propagation models, despite the targets being 3D and within the near field. This novel study uses full 3D electromagnetic (EM) wave simulation of the antenna arrays and propagation through the air and ground. Full EM simulation allows the sensitivity of radio measurements to landmine characteristics to be determined. The number and configuration of antenna elements are very important and must be optimised, contrary to the 2D sensitivity studies in (Watson, Lionheart 2014, Watson 2016) which conclude that the degree (number of elements) of the multi-static system is not critical. A novel sensitivity analysis for tilted handheld GPR antennas is used to demonstrate the positive impact of tilted antenna orientation on detection performance. A time domain GPR and A-scan data, consistent with a commercial handheld system, the MINEHOUND, is used throughout the simulated experiments which are based on synthetic GPR measurements.Finally, this thesis introduces a novel method of optimising the FWI solution through feature extraction or estimation of the internal air void typically present in pressure activated mines, to distinguish mines from non-mine targets and reduce the incidence of false positives

The Journal of Conventional Weapons Destruction, Issue 24.1 (2020)

Mine Action on the Korean Peninsula Raising the Profile of Mine Action A New Approach to IMAS Compliance Disposal of EO and Environmental Risk Mitigation Explosive Ordnance Risk Education - Measuring Behavior Chang

Design of a training tool for improving the use of hand-held detectors in humanitarian demining

Purpose - The purpose of this paper is to introduce the design of a training tool intended to improve deminers' technique during close-in detection tasks. Design/methodology/approach - Following an introduction that highlights the impact of mines and improvised explosive devices (IEDs), and the importance of training for enhancing the safety and the efficiency of the deminers, this paper considers the utilization of a sensory tracking system to study the skill of the hand-held detector expert operators. With the compiled information, some critical performance variables can be extracted, assessed, and quantified, so that they can be used afterwards as reference values for the training task. In a second stage, the sensory tracking system is used for analysing the trainee skills. The experimentation phase aims to test the effectiveness of the elements that compose the sensory system to track the hand-held detector during the training sessions. Findings - The proposed training tool will be able to evaluate the deminers' efficiency during the scanning tasks and will provide important information for improving their competences. Originality/value - This paper highlights the need of introducing emerging technologies for enhancing the current training techniques for deminers and proposes a sensory tracking system that can be successfully utilised for evaluating trainees' performance with hand-held detectors. © Emerald Group Publishing Limited.The authors acknowledge funding from the European Community's Seventh Framework Programme (FP7/2007‐2013 TIRAMISU) under Grant Agreement No. 284747 and partial funding under Robocity2030 S‐0505/DPI‐0176 and FORTUNA A1/039883/11 (Agencia Española de Cooperación Internacional para el Desarrollo – AECID). Dr Roemi Fernández acknowledges support from CSIC under grant JAE‐DOC. Dr Héctor Montes acknowledges support from Universidad Tecnológica de Panamá and from CSIC under grant JAE‐DOC.Peer Reviewe