Publication: Determination of Fruit Quality by Image Using Deep Neural Network
| dc.contributor.author | Snatkina, O. A. | |
| dc.contributor.author | Kugushev, A. R. | |
| dc.date.accessioned | 2024-12-25T13:48:07Z | |
| dc.date.available | 2024-12-25T13:48:07Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | © 2022 IEEE.This paper proposes an approach to recognizing and determining the freshness of fruits based on the YOLOv3 neural network, working with both images and videos. 6 classes are used as recognition objects: fresh and spoiled bananas, oranges, and apples. To train the network, its own marked-up dataset is used. A small amount of training data is enough for the network to show good quality metrics and correct work results. The precision of recognition and determination of fruit freshness was 90%, recall 96%. | |
| dc.format.extent | С. 1423-1426 | |
| dc.identifier.citation | Snatkina, O. A. Determination of Fruit Quality by Image Using Deep Neural Network / Snatkina, O.A., Kugushev, A.R. // Proceedings of the 2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2022. - 2022. - P. 1423-1426. - 10.1109/ElConRus54750.2022.9755693 | |
| dc.identifier.doi | 10.1109/ElConRus54750.2022.9755693 | |
| dc.identifier.uri | https://www.doi.org/10.1109/ElConRus54750.2022.9755693 | |
| dc.identifier.uri | https://www.scopus.com/record/display.uri?eid=2-s2.0-85129545136&origin=resultslist | |
| dc.identifier.uri | https://openrepository.mephi.ru/handle/123456789/27905 | |
| dc.relation.ispartof | Proceedings of the 2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2022 | |
| dc.title | Determination of Fruit Quality by Image Using Deep Neural Network | |
| dc.type | Conference Paper | |
| dspace.entity.type | Publication | |
| relation.isOrgUnitOfPublication | 010157d0-1f75-46b2-ab5b-712e3424b4f5 | |
| relation.isOrgUnitOfPublication.latestForDiscovery | 010157d0-1f75-46b2-ab5b-712e3424b4f5 |