THE ROLE OF COMPUTER VISION IN THE MODERN WORLD: ACHIEVEMENTS, CHALLENGES AND PROSPECTS

Abstract

This scientific article examines the essence of computer vision as a modern technology, its achievements, challenges and prospects. Computer vision plays an important role in various fields such as medicine, automotive, robotics and many others. The article examines the basic principles of computer vision, its application in various fields, comparative analysis of neural networks. The purpose of the study is to choose the topology of convolutional neural networks by conducting a comparative analysis of models of convolutional neural networks and their characteristics, which significantly affect the implementation of typical computer vision tasks. The methodology consists in a comparative analysis of the main classification methods based on wellknown convolutional neural networks, which are focused on the processing and recognition of image patterns, segmentation of text data, as well as sound streams. An analysis of existing research and publications on computer vision technologies, empirical research and evaluation of the effectiveness and accuracy of various approaches to computer vision has been carried out. The scientific novelty of the results obtained in the work consists in determining the main challenges facing the development of computer vision, in particular, the problem of processing large volumes of data, ensuring the accuracy and speed of algorithms, conducting a comprehensive analysis of modern achievements in the field of computer vision, taking into account the latest scientific research and technological development. Conclusions. Neural networks such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have their own advantages and disadvantages that may depend on specific tasks and data. Given this, it is advisable to use them depending on the problems or tasks that need to be solved, namely: ANNs (artificial neural networks) are useful for solving complex problems; CNNs (Convolutional Neural Networks) are the most effective way to solve computer vision problems; RNNs (recurrent neural networks) are capable of natural language processing, useful for time series forecasting.