Investigation of the effect of object size on accuracy of human localisation in images acquired from unmanned aerial vehicles

The use of unmanned aerial vehicles is gaining wide popularity in various areas of research and information acquisition. More and more often, unmanned aerial vehicles are used to obtain various types of images of the Earth’s surface for its study. In particular, such data can be used in law enforcement, localization of crowds, etc. Typically, such systems operate independently of humans and provide information about objects in an automatic mode, with humans working only under the control of the aircraft. One of the main components of such systems is a neural network for localization and classification of objects, the parameters of which determine both the accuracy of the system as a whole and the design of the aircraft for shooting. In particular, the accuracy of the neural network determines the profitability of such a system, because if the accuracy is insufficient, the use of such systems will not make sense. Therefore, the main subject of research in this paper is a neural network for object localization, in particular YOLO v5, and its accuracy parameters on images obtained from unmanned aerial vehicles. The main focus of this paper is on the parameters of the neural network and the study of its metrics, which are important parameters of a trained neural network. Another important parameter for the further use of a neural network is its training parameters, as well as the data used for training. This study also pays attention to the details of the training process. The main goal of this study is to train a neural network on a selected dataset and to study the accuracy metrics of the trained neural network. The main goal of this study is to determine the dependence of localization accuracy on the area of the object, which will allow for more detailed development of unmanned systems with automatic object detection, as well as to assess the profitability of using such systems in task planning. On the basis of the data obtained, conclusions were drawn about the dependence of localization accuracy on the area of an object in images from unmanned aerial vehicles. These data can serve as a reference for unmanned aerial vehicle developers, particularly when selecting photo modules or planning the system architecture.