Landmine Detection and Classification Using MLP

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.