Detection and classification of landmines using machine learning applied to metal detector data

ABSTRACT The current landmine clearance methods mostly rely on the manual use of metal detectors (MDs) and on the deminer’s experience in differentiating between the sounds emitted due to the presence of a landmine or of harmless clutter. This process suffers from high false-alarm rates, which renders the demining effort slow and costly. In this paper, we report our attempts in using machine learning for decision making in the demining process. We have created our own database of the MD responses corresponding to landmines and/or clutter. A robotic rail is designed and assembled to accurately measure these responses and build the database. Several machine learning models are then developed using the database with the aim of detecting the presence of landmines and classifying them. It is shown that the classification algorithms lead to accurately discriminating the landmines and distinguishing between different buried objects including mines or other items based on the metal detector delivered data or signature.