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Title	Smart City Management System Based On Multi-Purpose Deep Neural Network
ID_Doc	50360
Authors	Nikolaev E.; Konyrkhanova A.; Zakharov V.
Year	2022
Published	Proceedings - 2022 International Russian Automation Conference, RusAutoCon 2022
DOI	http://dx.doi.org/10.1109/RusAutoCon54946.2022.9896282
Abstract	Modern research in the field of classification, detection and semantic segmentation focuses on the use of recurrent neural networks as the basis for their approaches. Therefore, a deep understanding of the mechanisms of functioning of such deep models is essential for discovering new architectures of neural networks. This paper proposes a smart city control system architecture based on deep convolutional neural networks. The control system has a multilayer architecture that combines loosely coupled intelligent components. As the main layer, a solution based on deep learning technology is applied, which allows solving several tasks simultaneously: segmentation, detection and classification of images received from surveillance cameras of the smart city system. The data obtained at the output of this layer is used for further analysis and decision-making in the smart city system. The proposed architecture has a high degree of modularity and allows the replacement of individual elements in a loosely coupled architecture. In this paper, deep learning and computer vision technologies are also considered, on the basis of which the image processing layer from video cameras is implemented. A masked recurrent neural network is used for this task. © 2022 IEEE.
Author Keywords	classification; deep learning; detection; segmentation; smart city; traffic control

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