Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks

The article discusses the solution to the problem of choosing the architecture of a convolutional neural network for use in the computer vision of a smart vending refrigerator. Comparative tests decided the architectures of convolutional neural networks YOLOv2, YOLOv3, YOLOv4, Mask R-CNN, and YOLACT...

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Main Authors: Alekhina Anna E., Dorrer Mikhail G., Ovchinnikov Alexander G.
Format: Article
Language:English
Published: EDP Sciences 2023-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/27/e3sconf_agritechviii2023_03010.pdf
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author Alekhina Anna E.
Dorrer Mikhail G.
Ovchinnikov Alexander G.
author_facet Alekhina Anna E.
Dorrer Mikhail G.
Ovchinnikov Alexander G.
author_sort Alekhina Anna E.
collection DOAJ
description The article discusses the solution to the problem of choosing the architecture of a convolutional neural network for use in the computer vision of a smart vending refrigerator. Comparative tests decided the architectures of convolutional neural networks YOLOv2, YOLOv3, YOLOv4, Mask R-CNN, and YOLACT ++ on a standard MS COCO dataset, and then on datasets formed from images of typical smart refrigerator products. As a result of comparative tests, the best performance was demonstrated by the YOLOv3 architecture, trained based on a normalized dataset, supplemented with examples with complex intersections of samples without preprocessing examples. The obtained results substantiated the architecture used in computer vision of serially produced "smart" vending machines.
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spelling doaj.art-ea5f695442ec499f9f92bd887fdce6e22023-06-09T09:11:05ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013900301010.1051/e3sconf/202339003010e3sconf_agritechviii2023_03010Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networksAlekhina Anna E.0Dorrer Mikhail G.1Ovchinnikov Alexander G.2Reshetnev Siberian State University of Science and TechnologyReshetnev Siberian State University of Science and TechnologySolution FactoryThe article discusses the solution to the problem of choosing the architecture of a convolutional neural network for use in the computer vision of a smart vending refrigerator. Comparative tests decided the architectures of convolutional neural networks YOLOv2, YOLOv3, YOLOv4, Mask R-CNN, and YOLACT ++ on a standard MS COCO dataset, and then on datasets formed from images of typical smart refrigerator products. As a result of comparative tests, the best performance was demonstrated by the YOLOv3 architecture, trained based on a normalized dataset, supplemented with examples with complex intersections of samples without preprocessing examples. The obtained results substantiated the architecture used in computer vision of serially produced "smart" vending machines.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/27/e3sconf_agritechviii2023_03010.pdf
spellingShingle Alekhina Anna E.
Dorrer Mikhail G.
Ovchinnikov Alexander G.
Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks
E3S Web of Conferences
title Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks
title_full Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks
title_fullStr Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks
title_full_unstemmed Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks
title_short Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks
title_sort smart eco friendly refrigerator based on implementation of architectures of convolutional neural networks
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/27/e3sconf_agritechviii2023_03010.pdf
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