TY  - GEN
N2  - 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.
AV  - public
A1  - Picetti, F
A1  - Testa, G
A1  - Lombardi, F
A1  - Bestagini, P
A1  - Lualdi, M
A1  - Tubaro, S
UR  - https://doi.org/10.1109/TSP.2018.8441206
KW  - Deep Learning; Landmine Detection; GPR
ID  - discovery10059744
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
PB  - IEEE
T3  - International Conference on Telecommunications and Signal Processing
Y1  - 2018/08/23/
TI  - Convolutional Autoencoder for Landmine Detection on GPR Scans
ER  -