АЛГОРИТМ ФОРМУВАННЯ НАБОРУ АПРІОРНИХ ДАНИХ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ ОБРОБКИ ЦИФРОВИХ АЕРОЗНІМКІВ

Abstract

Analysis of the use of unmanned aerial systems in the combat zone indicates a constant increase in the requirements for intelligence data. One of the ways to increase the effectiveness of UaV application is the use of artificial intelligence methods. The basis for building a reliable neural network model is a large amount of various data, which helps to more accurately summarize information about the given task. The article considers an approach for increasing the accuracy of object image classification by convolutional neural networks based on data augmentation, which differs from existing ones in its adaptation to shooting factors and the specificity of aerial reconnaissance objects. An important and most time-consuming step in building an accurate machine learning model is finding and annotating the data that will be used to train and test the accuracy of the neural network. The accuracy and stability of the network in real conditions depends on the amount of collected data. To date, such methods of object image augmentation as geometric transformations, color correction, and spatial image filtering have been well described and analyzed. The possibilities of optimal combinations of data augmentation methods to achieve the desired generalization of poorly visible invariant features of objects remain unexplored at the moment. Taking into account the conducted research, an approach was proposed for the formation of a set of a priori data of a neural network for object recognition on digital aerial photographs, which will significantly reduce the complexity of the process of collecting the necessary data and replace it with magnification methods that are much simpler, consume less computing resources, and increase the accuracy of work convolutional neural networks, simulation of the use of the proposed approach was carried out. Keywords: unmanned aerial vehicle; convolutional neural network; data augmentation; automated processing of digital aerial photographs.