Implementation of a Vision System for a Landmine Detecting Robot Using Artificial Neural Network

TL;DR

This paper details the development of an autonomous landmine detection robot utilizing a vision system and artificial neural networks to identify and classify landmines with high accuracy in challenging conditions.

Contribution

It introduces a novel combination of digital image processing and neural network classification for landmine detection in an autonomous robot.

Findings

Achieved up to 90% success rate in landmine identification.

Effectively classified different landmine types under various conditions.

Demonstrated robustness of the vision system in real-world scenarios.

Abstract

Landmines, specifically anti-tank mines, cluster bombs, and unexploded ordnance form a serious problem in many countries. Several landmine sweeping techniques are used for minesweeping. This paper presents the design and the implementation of the vision system of an autonomous robot for landmines localization. The proposed work develops state-of-the-art techniques in digital image processing for pre-processing captured images of the contaminated area. After enhancement, Artificial Neural Network (ANN) is used in order to identify, recognize and classify the landmines' make and model. The Back-Propagation algorithm is used for training the network. The proposed work proved to be able to identify and classify different types of landmines under various conditions (rotated landmine, partially covered landmine) with a success rate of up to 90%.