Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/12082
Title: Automatic system for evaluating the quality of the display of goods in a smart store based on cascading neural networks
Authors: Talalaeva, J.
Талалаева, Ю.
Kuchukov, V. A.
Кучуков, В. А.
Kuchukova, E. A.
Кучукова, Е. А.
Vashchenko, I. S.
Ващенко, И. С.
Nazarov, A. S.
Назаров, А. С.
Keywords: Neural networks;Product layout;Semantic segmentation;Smart store;Video surveillance systems;Quality control
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
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
Series/Report no.: Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
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
URI: http://hdl.handle.net/20.500.12258/12082
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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