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.

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The different versions of the original document can be found in:

http://dx.doi.org/10.5281/zenodo.1159843 under the license https://creativecommons.org/licenses/by/4.0
http://dx.doi.org/10.5281/zenodo.1159844 under the license https://creativecommons.org/licenses/by/4.0

DOIS: 10.5281/zenodo.1159843 10.5281/zenodo.1159844 10.23919/eusipco.2017.8081259

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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.5281/zenodo.1159843
Licence: Other

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