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Landmine Detection from GPR Data Using Convolutional Neural Networks

Lameri, S; Lombardi, F; Bestagini, P; Lualdi, M; Tubaro, S; (2017) Landmine Detection from GPR Data Using Convolutional Neural Networks. In: 2017 25th European Signal Processing Conference (EUSIPCO). (pp. pp. 508-512). IEEE Green open access

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The presence of buried landmines is a serious threat in many areas around the World. Despite various techniques have been proposed in the literature to detect and recognize buried objects, automatic and easy to use systems providing accurate performance are still under research. Given the incredible results achieved by deep learning in many detection tasks, in this paper we propose a pipeline for buried landmine detection based on convolutional neural networks (CNNs) applied to ground-penetrating radar (GPR) images. The proposed algorithm is capable of recognizing whether a B-scan profile obtained from GPR acquisitions contains traces of buried mines. Validation of the presented system is carried out on real GPR acquisitions, albeit system training can be performed simply relying on synthetically generated data. Results show that it is possible to reach 95% of detection accuracy without training in real acquisition of landmine profiles.

Type: Proceedings paper
Title: Landmine Detection from GPR Data Using Convolutional Neural Networks
Event: EUSIPCO 2018, 25th European Signal Processing Conference, 28 August - 2 September 2017, Kos, Greece
Location: GREECE
Dates: 28 August 2017 - 02 September 2017
ISBN-13: 9781538607510
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/EUSIPCO.2017.8081259
Publisher version: https://doi.org/10.23919/EUSIPCO.2017.8081259
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Ground penetrating radar, Landmine detection, Training, Convolution, Pipelines
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
URI: https://discovery.ucl.ac.uk/id/eprint/10059743
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