An automated decision system for landmine detection and classification using metal detector signals

Abstract

An automated decision system for landmine detection and discrimination is implemented and evaluated using metal detector array data. The techniques utilised include: a gradient based peak isolation method, wavelet transforms, fuzzy ARTMAP neural networks, and the generic majority voting scheme. The features selected for representing the input data are composed of the morphological and wavelet based features of the target signature responses. Classification experiments are conducted in an attempt to discriminate target type and burial depth according to two different methodologies. The results obtained are promising, with the implemented decision system achieving high probabilities of detection with reasonable false alarm rates, and exceptional discrimination before and after decision fusion with relatively low classification errors. © 2010 Springer-Verlag Berlin Heidelberg.