Semantic Image Segmentation in Duckietown

Autor:	D. E. Shabalina, K. V. Chaika, T. V. Liakh, K. S. Lanchukovskaya
Rok vydání:	2021
Předmět:	History business.industry Computer science Semantic image segmentation Pattern recognition Artificial intelligence business Computer Science Applications Education
Zdroj:	Vestnik NSU. Series: Information Technologies. 19:26-39
ISSN:	2410-0420 1818-7900
DOI:	10.25205/1818-7900-2021-19-3-26-39
Popis:	The article is devoted to evaluation of the applicability of existing semantic segmentation algorithms for the “Duckietown” simulator. The article explores classical semantic segmentation algorithms as well as ones based on neural networks. We also examined machine learning frameworks, taking into account all the limitations of the “Duckietown” simulator. According to the research results, we selected neural network algorithms based on U-Net, SegNet, DeepLab-v3, FC-DenceNet and PSPNet networks to solve the segmentation problem in the “Duckietown” project. U-Net and SegNet have been tested on the “Duckietown” simulator.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a28ef764fda9cc3318d8ff9ac050a5ca https://doi.org/10.25205/1818-7900-2021-19-3-26-39 Zobrazit plný text záznamu