Fruit Classification Based on Improved YOLOv7 Algorithm

Authors

  • Shibo Guo Harbin University of Science and Technology
  • Tianyu Ren Harbin University of Science and Technology
  • Qing Wu Harbin University of Science and Technology
  • Xiaoyu Yu Zhongshan Institute
  • Aili Wang Harbin University of Science and Technology

DOI:

https://doi.org/10.14464/ess.v10i7.600

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.

Author Biographies

Shibo Guo, Harbin University of Science and Technology

Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application

Tianyu Ren, Harbin University of Science and Technology

Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application

Qing Wu, Harbin University of Science and Technology

Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application

Xiaoyu Yu, Zhongshan Institute

College of Electron and Information, University of Electronic Science and Technology of China

Aili Wang, Harbin University of Science and Technology

Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application

ISCSET 2023

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Published

2023-07-17