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

Abstract

This research paper proposes a new approach to object identification in static visual scenes that combines advanced machine learning and image processing techniques. Due to the two-stage structure, the proposed identification method provides high accuracy and speed of object detection in various lighting, distortion and noise conditions. The advantages of the new approach include high performance, efficient use of resources and scalability for different application areas. The purpose of the study is to increase the accuracy and speed of object identification in static visual scenes, resistance to various types of noise and distortions. The research involves the optimization of image processing and machine learning algorithms that allow for the efficient selection and classification of objects in conditions of limited resources and variability of visual scenes. The methodology consists in a comparative analysis of existing object identification methods, including both twostage and one-stage approaches. Their advantages and limitations are revealed. An empirical study and evaluation of the effectiveness and accuracy of various approaches to the detection and identification of objects was carried out. The scientific novelty of the results obtained in the work is the combination of one-stage and two-stage detection of objects using RefineDet, which allows to improve the accuracy and speed of object detection, compared to traditional one-stage methods. Conclusions. RefineDet is a powerful and efficient method for detecting objects in images. Its uniqueness lies in the combination of the advantages of one-stage and two-stage approaches, which ensures high accuracy and speed of work. This makes it suitable for use in various real-world applications where fast and accurate object identification is required.