UAV Integration with Neural Network in Landmine and Minefield Detection Tasks

Abstract

One of the newest stages in the improvement of unmanned aerial vehicles (UAVs) is the integration of such systems with the neural networks, which, in turn, is not a novelty, but provides such systems with a further level of practical application. Having conducted a meta-analysis of the results of previous studies and available information on this topic, it was found that in the modern period, in addition to successful practical implementations of the integration of artificial intelligence with UAVs, there is already a certain classification of such processes according to the principles of optimal improvement of UAV capabilities and by areas of society. In addition to the publicly available and well-known information about the successful use of drones in the military and logistics sectors of human activity, UAVs successfully perform tasks in such sectors as agriculture, engineering, search, etc. The main purpose of the article is to analyze, review, study and systematize existing information on the positive effectiveness and feasibility of using the principles, approaches and integration of unmanned aerial vehicles with machine learning technologies to improve the efficiency of solving the problems of locating and detecting landmines and minefields, which is a major humanitarian problem for civil society located in the territory where military conflicts are currently taking place or in the territories where military clashes or conflicts have occurred in the past. In this article, a small study was conducted to develop a prototype neural network that can be further integrated with UAVs for landmine and minefield detection tasks. The described neural network was trained on an open dataset, trained using the algorithms chosen in the study, and has a fairly good final result in terms of detection accuracy, which is 1.5% higher than the accuracy of publicly available neural networks in a review of similar developments or studies.