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Landmine Detection and Classification Using MLP

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3 Author(s)
Achkar, R. ; Dept. of Comput. & Commun. Eng., American Univ. of Sci. & Technol., Beirut, Lebanon ; Owayjan, M. ; Mrad, C.

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.

Published in:

Computational Intelligence, Modelling and Simulation (CIMSiM), 2011 Third International Conference on

Date of Conference:

20-22 Sept. 2011