Fruit Classification Based on Improved YOLOv7 Algorithm
Abstract: With the rapid development of technology and advancements, unmanned vending machines have emerged as the primary contactless retail method. The efficient and accurate implementation of automated identification technology for agricultural products in their distribution and sales has become an urgent problem that needs to be addressed. This article presents an improved YOLOv7 (You Only Look Once) algorithm for fruit detection in complex environments. By replacing the 3×3 convolutions in the backbone of YOLOv7 with Deformable ConvNet v2(DCNv2), the recognition accuracy and efficiency of fruit classification in YOLOv7 are significantly enhanced. The results indicate that the overall recognition accuracy of this system for ten types of fruits is 98.3%, showcasing its high precision and stability. https://www.bibliothek.tu-chemnitz.de/ojs/index.php/cs/article/view/600
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Fruit Classification Based on Improved YOLOv7 Algorithm ; volume:10 ; number:7 ; day:17 ; month:07 ; year:2023
Embedded selforganising systems ; 10, Heft 7 (17.07.2023)
- Creator
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Guo, Shibo
Ren, Tianyu
Wu, Qing
Yu, Xiaoyu
Wang, Aili
- DOI
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10.14464/ess.v10i7.600
- URN
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urn:nbn:de:101:1-2023092019454870833783
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:52 AM CEST
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Associated
- Guo, Shibo
- Ren, Tianyu
- Wu, Qing
- Yu, Xiaoyu
- Wang, Aili
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