| Issue |
E3S Web of Conf.
Volume 390, 2023
VIII International Conference on Advanced Agritechnologies, Environmental Engineering and Sustainable Development (AGRITECH-VIII 2023)
|
|
|---|---|---|
| Article Number | 03010 | |
| Number of page(s) | 6 | |
| Section | Information Technologies, Automation Engineering and Digitization of Agriculture | |
| DOI | https://doi.org/10.1051/e3sconf/202339003010 | |
| Published online | 01 June 2023 | |
Smart eco-friendly refrigerator based on implementation of architectures of convolutional neural networks
1 Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy Ave., Krasnoyarsk, 660037, Russian Federation
2 Siberian Federal University, 79 Svobodny pr., Krasnoyarsk, 660041, Russia
3 Solution Factory, Krasnoy Armii, 10c3, Krasnoyarsk, 660001, Russian Federation
* Corresponding author: dorrer_mg@sibsau.ru
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.
© The Authors, published by EDP Sciences, 2023
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