КФУ. Карточка публикации. Recognition of Slightly Blurred Images of Planets by a Convolutional Neural Network

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RECOGNITION OF SLIGHTLY BLURRED IMAGES OF PLANETS BY A CONVOLUTIONAL NEURAL NETWORK

Форма представления

Тезисы и материалы конференций в зарубежных журналах и сборниках

Год публикации

2024

Язык

английский

Авторы, сотрудники КФУ

Егорчев Антон Александрович, автор

Тумаков Дмитрий Николаевич, автор

Библиографическое описание на языке оригинала

Tuliabaeva, D. Recognition of Slightly Blurred Images of Planets by a Convolutional Neural Network / D. Tuliabaeva, D. Tumakov, A. Egorchev // III International Scientific and Practical Conference Technologies, Materials Science and Engineering (EEA-III-2024) : AIP Conference Proceedings, Dushanbe. Vol. 3243. – Melville: AIP PUBLISHING, 2024. – P. 20087. – DOI 10.1063/5.0247350

Аннотация

AIP Conference Proceedings. Том 3243. Melville, 2024

Ключевые слова

Image classification, Convolutional Neural Network

Название журнала

AIP Conference Proceedings. Том 3243. Melville, 2024

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https://repository.kpfu.ru/?p_id=317401

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Егорчев Антон Александрович	ru_RU
dc.contributor.author	Тумаков Дмитрий Николаевич	ru_RU
dc.date.accessioned	2024-01-01T00:00:00Z	ru_RU
dc.date.available	2024-01-01T00:00:00Z	ru_RU
dc.date.issued	2024	ru_RU
dc.identifier.citation	Tuliabaeva, D. Recognition of Slightly Blurred Images of Planets by a Convolutional Neural Network / D. Tuliabaeva, D. Tumakov, A. Egorchev // III International Scientific and Practical Conference Technologies, Materials Science and Engineering (EEA-III-2024) : AIP Conference Proceedings, Dushanbe. Vol. 3243. – Melville: AIP PUBLISHING, 2024. – P. 20087. – DOI 10.1063/5.0247350	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=317401	ru_RU
dc.description.abstract	AIP Conference Proceedings. Том 3243. Melville, 2024	ru_RU
dc.description.abstract	The influence of the degree of blur on accuracy of recognition of celestial bodies images is studied. For this purpose, color photographs of nine celestial objects of the Solar System are used including eight confirmed planets, two dwarf planets and the Earth's satellite - the Moon. The training and test datasets are represented by images of celestial bodies against a black background measuring 256 by 144 pixels. The analysis is carried out on color and corresponding black-and-white images. It is found that by increasing the filter size and adding additional convolutional layers to the neural network, recognition accuracy increases. The study confirms that the accuracy of celestial body recognition is highly dependent on even the degree of blur (3x3 matrix), decreasing from 99% to 79% for color images and from 97.5% to 40% for grayscale images. It is shown that a dataset enlarged by adding images obtained through rotations by 45, 90, 180 and 270 degrees leads to a significant (by about 20%) increase in the accuracy of the model for blurred images. Histograms are presented demonstrating recognition accuracy depending on the degree of blur.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Image classification	ru_RU
dc.subject	Convolutional Neural Network	ru_RU
dc.title	Recognition of Slightly Blurred Images of Planets by a Convolutional Neural Network	ru_RU
dc.type	Тезисы и материалы конференций в зарубежных журналах и сборниках	ru_RU