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ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 9. Vol. 30. 2024

DOI: 10.17587/it.30.480-485

R. A. Dyachenko1, Dr. Sc., Professor, V. V. Dovgal1, Magister, D. A. Gura1,2, Cand. Sc., Assistant Professor,
1Kuban State Technological University,
2Kuban State Agrarian University

On the Issue of Comparing the Effectiveness of YOLOv8 and U-Net Neural Networks in the Tasks of Segmentation of Territorial Objects

There are many problems associated with the automation of aerial photography. Solutions based on machine learning algorithms turned out to be particularly effective for solving them. However, choosing a suitable computer vision model is a difficult task due to the variety of models available. The purpose of this scientific work is to select the most popular neural network architectures and identify the most effective architecture in terms of efficiency and quality of work performed.
Keywords: U-Net, YOLOv8, artificial intelligence, computer vision, image detection, image segmentation, information technology, aerial photography

P. 480-485

Acknowlegements: The research is carried out with the financial support of the Kuban Science Foundation in the framework of the scientific and innovation project Num. NIP-20.1/22.16.

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