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Neural Network Technologies for Detection and Classification of Objects

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Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

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.

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Funding

The research was carried out within the state assignment of Ministry of Science and Higher Education of the Russian Federation (project no. 121022000116-0) at the Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences.

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  1. Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, Russia

    S. M. Borzov & E. S. Nezhevenko

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  1. S. M. Borzov
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  2. E. S. Nezhevenko
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Correspondence to S. M. Borzov or E. S. Nezhevenko.

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Translated by L. Trubitsyna

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Borzov, S.M., Nezhevenko, E.S. Neural Network Technologies for Detection and Classification of Objects. Optoelectron.Instrument.Proc. 59, 329–345 (2023). https://doi.org/10.3103/S8756699023030032

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