Landmine Detection From Gpr Data Using Convolutional Neural Networks


== Abstract ==

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.

Document type: Conference object

== Full document ==
<pdf>Media:Tubaro_et_al_2018a-beopen3008-7566-document.pdf</pdf>


== Original document ==

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

* [http://dx.doi.org/10.23919/eusipco.2017.8081259 http://dx.doi.org/10.23919/eusipco.2017.8081259]

* [https://discovery.ucl.ac.uk/id/eprint/10059743/1/2017_Eusipco.pdf https://discovery.ucl.ac.uk/id/eprint/10059743/1/2017_Eusipco.pdf]

* [https://zenodo.org/record/1159844 https://zenodo.org/record/1159844] under the license http://creativecommons.org/licenses/by/4.0/legalcode

* [http://xplorestaging.ieee.org/ielx7/8067434/8081145/08081259.pdf?arnumber=8081259 http://xplorestaging.ieee.org/ielx7/8067434/8081145/08081259.pdf?arnumber=8081259],
: [http://dx.doi.org/10.23919/eusipco.2017.8081259 http://dx.doi.org/10.23919/eusipco.2017.8081259]

* [https://discovery.ucl.ac.uk/id/eprint/10059743 https://discovery.ucl.ac.uk/id/eprint/10059743],
: [https://discovery.ucl.ac.uk/id/eprint/10059743/1/2017_Eusipco.pdf https://discovery.ucl.ac.uk/id/eprint/10059743/1/2017_Eusipco.pdf]

* [https://ieeexplore.ieee.org/document/8081259 https://ieeexplore.ieee.org/document/8081259],
: [https://dblp.uni-trier.de/db/conf/eusipco/eusipco2017.html#LameriLBLT17 https://dblp.uni-trier.de/db/conf/eusipco/eusipco2017.html#LameriLBLT17],
: [http://ieeexplore.ieee.org/document/8081259 http://ieeexplore.ieee.org/document/8081259],
: [https://doi.org/10.23919/EUSIPCO.2017.8081259 https://doi.org/10.23919/EUSIPCO.2017.8081259],
: [https://discovery.ucl.ac.uk/id/eprint/10059743 https://discovery.ucl.ac.uk/id/eprint/10059743],
: [https://re.public.polimi.it/handle/11311/1045977 https://re.public.polimi.it/handle/11311/1045977],
: [https://academic.microsoft.com/#/detail/2766854032 https://academic.microsoft.com/#/detail/2766854032]

* [https://zenodo.org/record/1159844 https://zenodo.org/record/1159844],
: [http://dx.doi.org/10.5281/zenodo.1159843 http://dx.doi.org/10.5281/zenodo.1159843] under the license https://creativecommons.org/licenses/by/4.0

* [https://zenodo.org/record/1159844 https://zenodo.org/record/1159844],
: [http://dx.doi.org/10.5281/zenodo.1159844 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