Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал:
https://dspace.chmnu.edu.ua/jspui/handle/123456789/1783
Назва: | Neural Network Architectures for Recognizing Military Objects on Satellite Images |
Автори: | Kovaliv, O. Kondratenko, Y. Shevchenko, A. Sidenko, I. Kondratenko, G. |
Ключові слова: | artificial intelligence classification military objects neural networks satellite images |
Дата публікації: | 2023 |
Видавництво: | IEEE |
Короткий огляд (реферат): | This paper is devoted to the research and comparison of neural network architectures for recognizing military objects on satellite images. The paper analyzes the current state of the problem of object recognition, in particular, military objects on satellite images. In addition, the authors researched existing technologies and tools for recognizing military objects on satellite images, and also implemented models of neural networks with different architectures for detecting military objects. The influence of learning indicators of neural network models on the recognition and detection of objects on satellite and aerial photographs was investigated. As a result of the work, one multilayer perceptron, three models of convolutional neural networks, and neural networks with VGG and XCeption architectures were implemented and investigated, their main advantages and disadvantages were determined, and software with corresponding neural network architectures was developed using the Google Colab cloud service. The best results were shown by sixth model with a convolutional neural network architecture, the main difference of which was a gradual reduction in the number of filters and the size of the kernel on the convolutional layers. |
Опис: | Kovaliv, O., Kondratenko, Y., Shevchenko, A., Sidenko, I., & Kondratenko, G. (2023). Neural Network Architectures for Recognizing Military Objects on Satellite Images. Proceedings of the IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, 175-180. IEEE. Dortmund. DOI: 10.1109/IDAACS58523.2023.10348905 |
URI (Уніфікований ідентифікатор ресурсу): | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184795193&doi=10.1109%2fIDAACS58523.2023.10348905&partnerID https://ieeexplore.ieee.org/document/10348905 https://dspace.chmnu.edu.ua/jspui/handle/123456789/1783 |
ISBN: | 979-835035805-6 |
ISSN: | 27704262 |
Розташовується у зібраннях: | Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus |
Файли цього матеріалу:
Файл | Опис | Розмір | Формат | |
---|---|---|---|---|
Kovaliv, O., Kondratenko, Y., Shevchenko, A., Sidenko, I., Kondratenko, G..pdf | 60.2 kB | Adobe PDF | Переглянути/Відкрити |
Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.