JACIII Vol.27 No.3 pp. 474-480
doi: 10.20965/jaciii.2023.p0474

Research Paper:

Real-Time Wheat Unsound Kernel Classification Detection Based on Improved YOLOv5

Zhaohui Zhang*1,*2,†, Zengyang Zuo*2, Zhi Li*3, Yuguo Yin*4, Yan Chen*1, Tianyao Zhang*1, and Xiaoyan Zhao*1,*2 ORCID Icon

*1School of Automation and Electrical Engineering, University of Science and Technology Beijing
30 Xueyuan Road, Haidian District, Beijing 100083, China

*2Shunde Innovation School, University of Science and Technology Beijing
2 Zhihui Road, Daliang, Shunde District, Fo Shan, Guangdong 528399, China

*3College of Information Science and Engineering, Henan University of Technology
100 Lianhua Road, Zhengzhou High-Tech Development Zone, Zhengzhou, Henan 450001, China

*4Shandong Start Measurement and Control Equipment Co., Ltd.
600 Xinyi Road, Weifang Economic Development Zone, Weifang, Shandong 261101, China

Corresponding author

January 21, 2023
February 4, 2023
May 20, 2023
wheat classification, unsound kernels, YOLOv5, attention mechanism

China is one of the largest wheat production countries in the world. The wheat quality determines the price and many other aspects. The detection methods of wheat quality mainly depend on manual labor. It costs high amount of manpower and time, and the classification results are partly affected by different individuals. With the development of machine vision, an automatic classification system was presented in this study. A wheat unsound kernel identification method based on the improved YOLOv5 algorithm was designed by adding efficient channel attention (ECA). Compared with convolutional block attention module (CBAM) and squeeze-and-excitation network (SENet), the improved YOLOv5 algorithm was selected to fit the model better. The recognition results showed that YOLOv5 with the addition of the attention mechanism had a significant improvement in average accuracy over that without it. The most significant improvement was observed with the addition of ECA-YOLOv5, with an average accuracy of 96.24%, a 10% improvement over the other two models, and a 13% improvement over the original YOLOv5. This satisfied the application requirements for detection of wheat unsound kernel.

Real-time unsound kernel detection

Real-time unsound kernel detection

Cite this article as:
Z. Zhang, Z. Zuo, Z. Li, Y. Yin, Y. Chen, T. Zhang, and X. Zhao, “Real-Time Wheat Unsound Kernel Classification Detection Based on Improved YOLOv5,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 474-480, 2023.
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Last updated on May. 10, 2024