  Buried landmines and unexploded remnants of war are a constant threat for the
population of many countries that have been hit by wars in the past years. The
huge amount of human lives lost due to this phenomenon has been a strong
motivation for the research community toward the development of safe and robust
techniques designed for landmine clearance. Nonetheless, being able to detect
and localize buried landmines with high precision in an automatic fashion is
still considered a challenging task due to the many different boundary
conditions that characterize this problem (e.g., several kinds of objects to
detect, different soils and meteorological conditions, etc.). In this paper, we
propose a novel technique for buried object detection tailored to unexploded
landmine discovery. The proposed solution exploits a specific kind of
convolutional neural network (CNN) known as autoencoder to analyze volumetric
data acquired with ground penetrating radar (GPR) using different
polarizations. This method works in an anomaly detection framework, indeed we
only train the autoencoder on GPR data acquired on landmine-free areas. The
system then recognizes landmines as objects that are dissimilar to the soil
used during the training step. Experiments conducted on real data show that the
proposed technique requires little training and no ad-hoc data pre-processing
to achieve accuracy higher than 93% on challenging datasets.
