Landmine Detection and Classification Using MLP

Abstract

This paper expounds on the design and the implementation of the intelligence (vision and brain) of an autonomous robot for landmines localization, specifically anti-tank mines, cluster bombs, or unexploded ordnance. The landmine sweeping technique under study utilizes state-of-the-art techniques in digital image processing for pre-processing captured images of the area being scanned. After enhancing the scanned images, data is fed into a processing unit that implements the Artificial Neural Network (ANN) in order to classify the landmines' make and model. The Back-Propagation algorithm is used for teaching the network. The system proved to be able to identify and classify different types of landmines under various conditions with a success rate of up to 90%. Various conditions include different viewpoints of the landmine such as having a rotated landmine, or a partially covered landmine.