Picetti, F;
Testa, G;
Lombardi, F;
Bestagini, P;
Lualdi, M;
Tubaro, S;
(2018)
Convolutional Autoencoder for Landmine Detection on GPR Scans.
In:
2018 41st International Conference on Telecommunications and Signal Processing (TSP).
IEEE
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Abstract
Buried unexploded landmines are a serious threat in many countries all over the World. As many landmines are nowadays mostly plastic made, the use of ground penetrating radar (GPR) systems for their detection is gaining the trend. However, despite several techniques have been proposed, a safe automatic solution is far from being at hand. In this paper, we propose a landmine detection method based on convolutional autoencoder applied to B-scans acquired with a GPR. The proposed system leverages an anomaly detection pipeline: the autoencoder learns a description of B-scans clear of landmines, and detects landmine traces as anomalies. In doing so, the autoencoder never uses data containing landmine traces at training time. This allows to avoid making strong assumptions on the kind of landmines to detect, thus paving the way to detection of novel landmine models.
| Type: | Proceedings paper |
|---|---|
| Title: | Convolutional Autoencoder for Landmine Detection on GPR Scans |
| Event: | TSP 2018, 41st International Conference on Telecommunications and Signal Processing, 4-6 July 2018, Athens, Greece |
| ISBN-13: | 9781538646960 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TSP.2018.8441206 |
| Publisher version: | https://doi.org/10.1109/TSP.2018.8441206 |
| 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: | Deep Learning; Landmine Detection; GPR |
| UCL classification: | UCL 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/10059744 |
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