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DC Field | Value | Language |
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dc.contributor.author | Talalaeva, J. | - |
dc.contributor.author | Талалаева, Ю. | - |
dc.contributor.author | Kuchukov, V. A. | - |
dc.contributor.author | Кучуков, В. А. | - |
dc.contributor.author | Kuchukova, E. A. | - |
dc.contributor.author | Кучукова, Е. А. | - |
dc.contributor.author | Vashchenko, I. S. | - |
dc.contributor.author | Ващенко, И. С. | - |
dc.contributor.author | Nazarov, A. S. | - |
dc.contributor.author | Назаров, А. С. | - |
dc.date.accessioned | 2020-06-19T12:25:29Z | - |
dc.date.available | 2020-06-19T12:25:29Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Talalaeva, J., Kuchukov, V., Kuchukova, E., Vashchenko, I., Nazarov, A. Automatic System for Evaluating the Quality of the Display of Goods in a Smart Store Based on Cascading Neural Networks // Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020. - 2020. - Номер статьи 9039266. - Pages 530-532 | ru |
dc.identifier.uri | http://hdl.handle.net/20.500.12258/12082 | - |
dc.description.abstract | In a new smart store, profit depends on the quality of the goods displayed on the shelf. If there is a product, but it is not laid out on shelves, the store suffers losses. In the paper, a control system is being developed for the timely display of goods on store shelves and for tracking in real-time the voids formed. To implement an automatic system for assessing the quality of filling store shelves with goods, we used cascading neural networks that performed two roles: a segmentation and a classifier, supplemented by an algorithmic solution to identify areas of potential voids. To train the classifier using the new algorithm, a training sample of 30 thousand images was created, 3 thousand images were used for validation, quality control on 10 thousand images. The proposed algorithm allowed us to obtain a quality of 96.7%. The developed system for assessing the quality of goods laying out on store shelves allows real-time assessment of the condition of shelves | ru |
dc.language.iso | en | ru |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | ru |
dc.relation.ispartofseries | Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020 | - |
dc.subject | Neural networks | ru |
dc.subject | Product layout | ru |
dc.subject | Semantic segmentation | ru |
dc.subject | Smart store | ru |
dc.subject | Video surveillance systems | ru |
dc.subject | Quality control | ru |
dc.title | Automatic system for evaluating the quality of the display of goods in a smart store based on cascading neural networks | ru |
dc.type | Статья | ru |
vkr.inst | Институт математики и естественных наук | ru |
Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
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scopusresults 1253 .pdf Restricted Access | 626.23 kB | Adobe PDF | View/Open |
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