Abstract and keywords
Abstract (English):
This article discusses the application of the YOLO (You Only Look Once) neural network architecture for computer vision tasks. YOLO is one of the most effective models for real-time object detection. The article describes the basic principles of the network, methods for improving accuracy and speed, as well as prospects for its future development.

Keywords:
computer vision, artificial intelligence, machine learning
References

1. Evstratkin, K. S. OPENCV: varianty ispol'zovaniya komp'yuternogo zreniya / K. S. Evstratkin, A. R. Sultanova, A. V. Erpelev // Cifrovye tehnologii: nauka, obrazovanie, innovacii : Materialy III Mezhdunarodnogo nauchnogo Foruma professorsko-prepodavatel'skogo sostava i molodyh uchenyh, Moskva, 09 noyabrya 2020 goda / Pod redakciey A.V. Oleynik, A.A. Zelenskogo. – Moskva: Moskovskiy gosudarstvennyy tehnologicheskiy universitet "STANKIN", 2021. – S. 28-31. – EDN KTLCNJ.

2. Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques / I. V. Arinichev, S. V. Polyanskikh, G. V. Volkova, I. V. Arinicheva // International Journal of Fuzzy Logic and Intelligent Systems. – 2021. – Vol. 21, No. 1. – P. 1-11. – DOIhttps://doi.org/10.5391/IJFIS.2021.21.1.1. – EDN CPIVQC.

3. Medvedeva, I. A. Automated complex development project based on computer vision technology / I. A. Medvedeva, M. V. Vanslav, M. A. Ragozina // Natural and Technical Sciences. – 2022. – No. 7(170). – P. 177-178. – EDN HONPYY.

4. He K., Zhang X., Ren S., Sun J. Glubokoe ostatochnoe obuchenie dlya raspoznavaniya izobrazheniy. CVPR, 2016.

5. Bochkarev S., Lempitskiy V. Glubokoe obuchenie v analize izobrazheniy: tendencii i prilozheniya. Raspoznavanie obrazov, 2021.

6. Minakova, O. V. Povyshenie effektivnosti raboty v proektah open sourse na osnove arhitekturnogo analiza (na primere proekta Sahana) / O. V. Minakova, I. V. Pocebneva, P. Yu. Gusev // Modelirovanie sistem i processov. – 2024. – T. 17, № 1. – S. 84-92. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-1-84-92. – EDN NZNSKM.

7. Zarevich A.I., Makarenko F.V., Yagodkin A.S., Zol'nikov K.V. Modelirovanie povedeniya mobil'nyh robotov s ispol'zovaniem geneticheskih algoritmov // Modelirovanie sistem i processov. – 2022. – T. 15, № 3. – S. 7-16. DOI: https://doi.org/10.12737/2219-0767-2022-15-3-7-16; EDN: https://elibrary.ru/AGVATZ

8. Sazonova, S. A. Modelirovanie processa diagnostiki utechek na osnove dvuhal'ternativnoy gipotezy s uchetom pomeh ot stohastichnosti potrebleniya v gidravlicheskoy sisteme / S. A. Sazonova, I. V. Scherbakova, G. I. Smetankina // Modelirovanie sistem i processov. – 2024. – T. 17, № 1. – S. 111-120. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-1-111-120. – EDN CSKRIZ.

9. Vybor kriteriya optimal'nosti pri prinyatii upravlencheskih resheniy v slozhnyh tehnicheskih sistemah / A. V. Skrypnikov, I. A. Vysockaya, S. A. Evdokimova [i dr.] // Modelirovanie sistem i processov. – 2024. – T. 17, № 1. – S. 120-128. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-1-120-128. – EDN MMIAIH.

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