USING NEURAL NETWORKS FOR LANDMINE DETECTION

The issue of large mined areas and their expansion is critical due to their impact on safety and the economy. This article focuses on enhancing the efficiency of optical methods for detecting surface-laid landmines in humanitarian demining tasks. It demonstrates that one of the most promising optical methods for mine detection is hyperspectral imaging, which provides exceptionally precise analysis of the spectral composition of the radiation in the background-target environment. However, practical application of hyperspectral methods is significantly complicated by the vast size of mined territories. Therefore, mine detection must be automated. To address this challenge, the potential of artificial neural networks was investigated. The study proposes the integration of artificial intelligence, specifically RBF (Radial Basis Function) networks, to improve the efficiency and accuracy of object detection. The work compares traditional classification methods with innovative machine learning approaches. Algorithms were tested on both simulated and real data, enabling an evaluation of their ability to identify objects under varying spectral content conditions. The specific characteristics of the study area define the requirements for assessing the performance of technical tools: foremost, the probability of missing a signal must be minimized. At the same time, given the large-scale computations involved, the probability of false alarms should remain low. The study shows that RBF neural networks can detect mines with a low rate of false alarms. During network training with a large spread parameter, the sensitivity of the output to the spectral variability of pixels decreases. This allows the network to detect a target even at low fill coefficients. Thus, the research results indicate that the proposed methods significantly reduce false alarms and ensure high performance under real-world conditions.