Abstract
This paper considers the problem of object recognition in images, which is one of the key problems of computer vision. The relevance of the research is due to the wide application of object recognition systems in such areas as security, medicine, robotics, automotive industry and quality control. The research analyses existing recognition methods, including traditional approaches and modern deep learning methods. Their advantages, disadvantages and effectiveness in different environments are evaluated. On the basis of experimental data, the most effective algorithms for application in recognition systems were selected. The results of the work allowed us to propose recommendations for the selection and improvement of methods of object recognition, which helps to improve the accuracy and reliability of such systems. The obtained conclusions can be useful for specialists in the field of computer vision and developers of applications that use recognition technologies.