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Neural network recognition system for video transmitted through a binary symmetric channel
V.A. Baboshina 1, A.R. Orazaev 1, P.A. Lyakhov 1,2, E.E. Boyarskaya 2

1 North-Caucasus Center for Mathematical Research, North-Caucasus Federal University,
355017 Stavropol, Russia, Pushkin st. 1;
2 Department of Mathematical Modeling, North-Caucasus Federal University,
355017 Stavropol, Russia, Pushkin st. 1

 PDF, 61 MB

DOI: 10.18287/2412-6179-CO-1388

Pages: 582-591.

Full text of article: English language.

Abstract:
The demand for transmitting video data is increasing annually, necessitating the use of high-quality equipment for reception and processing. The paper presents a neural network recognition system for videos transmitted via a binary symmetrical channel. The presence of digital noise in the data makes it challenging to recognize objects in videos even with advanced neural networks. The proposed system consists of a noise interference detector, a noise purification system based on an adaptive median filter, and a neural network for recognition. The experiment results demonstrate that the proposed system effectively reduces video noise and accurately identifies multiple objects. This versatility makes the system applicable in various fields such as medicine, life safety, physics, and chemistry. The direction of further research may be to improve the model neural network, increasing the database for training or using other noises for modeling.

Keywords:
neural networks, video recognition, YOLO, binary symmetric channel, video denoise.

Citation:
Baboshina VA, Orazaev AR, Lyakhov PA, Boyarskaya EE. Neural network recognition system for video transmitted through a binary symmetric channel. Computer Optics 2024; 48(4): 582-591. DOI: 10.18287/2412-6179-CO-1388.

Acknowledgements:
The research in Section “Experimental modeling of recognition system for noised video” was supported by the North-Caucasus Center for Mathematical Research under agreement number 075-02-2023-938 with the Ministry of Science and Higher Education of the Russian Federation. The rest of the paper was funded by the Russian Science Foundation (Project No. 23-71-10013).

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