Abstract
An automatic detection methodology for “legbreaker” Antipersonnel Landmines (APL) was developed based on digital image processing techniques and pattern recognition, applied to thermal images acquired by means of an Unmanned Aerial Vehicle (UAV) equipped with a thermal camera. The images were acquired from the inspection of a natural terrain with sparse vegetation and under uncontrolled conditions, in which prototypes of “legbreaker” APL were buried at different depths. Remarkable results were obtained using a Multilayer Perceptron (MLP) classifier, reaching a 97.1% success rate in detecting areas with the presence of these artifacts.