Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric Data

Bestagini, Paolo; Lombardi, Federico; Lualdi, Maurizio; Picetti, Francesco; Tubaro, Stefano

doi:10.1109/tgrs.2020.2984951

Cited by 39 publications

(14 citation statements)

References 63 publications

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“…As only mine-free images are used during training, the decoder will inevitably introduce errors in the reconstructed image whenever it contains evidence of mines, and such errors can be used as the anomaly indicator. For this reason, the distance value e will grow as data containing traces of mines are analysed, as more thoroughly explained in [61]. According to the specific threshold that is applied to the distance metric, and that is selected upon a set of training profiles at the system tuning time, the data will finally be classified as either including a target or not.…”

Section: Detection Performance Resultsmentioning

confidence: 99%

Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications

et al. 2020

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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.

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Section: Detection Performance Resultsmentioning

confidence: 99%

Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications

et al. 2020

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“…GPR has been proposed as an alternative to classical electromagnetic induction techniques for landmine detection due to a number of advantages, the principal one being the ability to detect landmines with little or no metal content, as long as there is a difference in the electromagnetic contrast between the ground and the target [120,143,144]. In addition, advanced imaging algorithms and data post-processing has allowed not only the localization of the target but also the extraction of valuable features that can pave the way for target recognition and identification [145][146][147][148][149]. However, standalone GPR platforms for antipersonnel landmine detection are less common than their metal detector counterparts, 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 [150].…”

Section: Gpr Applications In Forensic and Securitymentioning

confidence: 99%

From Its Core to the Niche: Insights from GPR Applications

2022

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Thanks to its non-destructive, high-resolution imaging possibilities and its sensitivity to both conductive and dielectric subsurface structures, Ground-Penetrating Radar (GPR) has become a widely recognized near-surface geophysical tool, routinely adopted in a wide variety of disciplines. Since its first development almost 100 years ago, the domain in which the methodology has been successfully deployed has significantly expanded from ice sounding and environmental studies to precision agriculture and infrastructure monitoring. While such expansion has been clearly supported by the evolution of technology and electronics, the operating principles have always secured GPR a predominant position among alternative inspection approaches. The aim of this contribution is to provide a large-scale survey of the current areas where GPR has emerged as a valuable prospection methodology, highlighting the reasons for such prominence and, at the same time, to suggest where and how it could be enhanced even more.

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“…In recent years, different kinds of UXO detection methods, such as ground-penetrating radar (GPR) [4,5], magnetic detection [6,7], and electromagnetic induction (EMI) [8,9], have been widely developed and applied with the development of detection technology. GPR can locate underground targets by transmitting high-frequency electromagnetic waves, which are easily affected by geological conditions.…”

Section: Introductionmentioning

confidence: 99%

Underground Target Localization Based on Improved Magnetic Gradient Tensor With Towed Transient Electromagnetic Sensor Array

et al. 2022

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Errors of target localization with the traditional magnetic gradient tensor mainly comes from three aspects, namely, the large equivalent error of the magnetic gradient tensor for shallow targets, the low signal-to-noise ratio (SNR) of the target response for deep targets, and the overlapping responses of multitargets. In this study, a towed transient electromagnetic sensor with a 3 × 3 receiving coils array is constructed. On the basis of the sensor array, an improved magnetic gradient tensor is proposed to accurately locate targets. For shallow targets, the magnetic gradient tensor is constructed using the responses of four adjacent receiving coils to reduce the equivalent error of the magnetic gradient tensor. For deep targets, all the responses of nine receiving coils are used to improve the SNR. Both the early and late time responses are used to roughly estimate the positions of multi-targets to improve the localization accuracy of the overlapping responses of multi-targets. Experimental results show that for underground targets within 2 m, the depth errors of the targets do not exceed 10 cm, and the horizontal errors of the targets are mostly within 10 cm, even if the responses of two adjacent targets overlap each other, indicating that the proposed method can effectively improve the localization accuracy of underground targets.

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Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric Data

Cited by 39 publications

References 63 publications

Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications

Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications

From Its Core to the Niche: Insights from GPR Applications

Underground Target Localization Based on Improved Magnetic Gradient Tensor With Towed Transient Electromagnetic Sensor Array

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