Abstract
Among all the forensic applications in which it has become an important exploration tool, ground penetrating radar (GPR) methodology is being increasingly adopted for buried landmine localisation, a framework in which it is expected to improve the operations efficiency, given the high resolution imaging capability and the possibility of detecting both metallic and non-metallic landmines. In this context, this study presents landmine detection equipment based on multi-polarisation: a ground coupled GPR platform, which ensures suitable penetration/resolution performance without affecting the safety of surveys, thanks to the inclusion of a flexible ballistic shielding for supporting eventual blasts. The experimental results have shown that not only can the blanket absorb blast-induced flying fragments impacts, but that it also allows for the acquisition of data with the accuracy required to generate a correct 3D reconstruction of the subsurface. The produced GPR volume is then processed through an automated learning scheme based on a Convolutional Neural Network (CNN) capable of detecting buried objects with a high degree of accuracy.
Highlights
Ground Penetrating Radar (GPR) technology, a non-destructive technique based on the propagation and reflection of electromagnetic waves [1,2], has been successfully adopted as a subsurface prospection tool to assist forensic investigation in a broad range of security applications, from buried explosive threats to human remains detection, as well as locating and tracking people in disaster areas [3,4]
Standalone ground penetrating radar (GPR) platforms for antipersonnel landmine detection are less common than their metal detector counterparts [21], a situation possibly related to the fact that GPR signal interpretation remains a complex task, and that the mentioned benefits are often balanced by its susceptibility to clutter [22,23], i.e., reflections coming from events that are unrelated to the target scattering characteristics but which occur in the same time window and have similar characteristics to the target wavelet
To characterise the performance of the developed system, a method based on convolutional neural networks (CNNs) applied to GPR data, to detect the presence of buried objects not coherent with the surrounding ground, has been evaluated
Summary
Ground Penetrating Radar (GPR) technology, a non-destructive technique based on the propagation and reflection of electromagnetic waves [1,2], has been successfully adopted as a subsurface prospection tool to assist forensic investigation in a broad range of security applications, from buried explosive threats to human remains detection, as well as locating and tracking people in disaster areas [3,4] It is evident, the wide variety of possible targets and scenarios that GPR equipment developed for security tasks has to deal with, which implies a degree of flexibility hardly achievable with other geophysical alternatives [5,6,7,8]. Stable in time, might reduce the detection threshold of the system, resulting in an unacceptably high False Alarm Rate (FAR) [24]
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