The article presents the results of a numerical experiment comparing the accuracy of neural network recognition of objects in images using various types of data set extensions. It describes the need to expand data sets using adaptive approaches in order to minimize the use of image transformations that may reduce the accuracy of object recognition. The author considers such approaches to data set expansion as random and automatic augmentation, as they are common, as well as the developed method of adaptive data set expansion using a reinforcement learning algorithm. The algorithms of operation of each of the approaches, their advantages and disadvantages of the methods are given. The work and main parameters of the developed method of expanding the dataset using the Deep-Q-Network algorithm are described from the point of view of the algorithm and the main module of the software package. Attention is being paid to one of the machine learning approaches, namely reinforcement learning. The application of a neural network for approximating the Q-function and updating it in the learning process, which is based on the developed method, is described. The experimental results show the advantage of using data set expansion using a reinforcement learning algorithm using the example of the Squeezenet v1.1 classification model. The comparison of recognition accuracy using data set expansion methods was carried out using the same parameters of a neural network classifier with and without the use of pre-trained weights. Thus, the increase in accuracy in comparison with other methods varies from 2.91% to 6.635%.

Keywords: dataset, extension, neural network models, classification, image transformation, data replacement

Currently, there is an increase in the number of scientific papers on models, methods and software and hardware for image processing and analysis. This is due to the widespread introduction of computer vision technologies into information processing and control systems. At the same time, approaches that provide fast image processing in real time using limited computing resources are relevant. Such approaches are usually based on low-level image filtering algorithms. One of the tasks to be solved in computer vision-based systems is the localization of round objects. These objects have the property of radial symmetry. Therefore, the approach based on the Fast Radial Symmetry Transform, which is considered in this paper, is effective for solving this problem. The paper describes the basic steps of the basic transformation, provides a procedure for determining the centers of radially symmetric areas for localization of round objects in images, and discusses examples of its application.

Keywords: computer vision, image processing, image analysis, localization of objects, methods of localization of round objects, fast radial symmetry transf, detecion of the centers of radially symmetric areas