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We present a review of the basic ideas used in solving the problems of detecting and classifying objects by their images using neural network technologies. The key publications on the most popular ways to improve classification accuracy are considered. It is shown that in the last decade, neural network methods for detecting objects have achieved significant success by using convolution technologies and applying deep learning with large databases. The main shortcomings, limitations and possible directions for the improvement of existing approaches are analyzed.
Борзов et al. (Thu,) studied this question.