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

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 d...

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Veröffentlicht in:Journal of advanced computational intelligence and intelligent informatics Jg. 27; H. 3; S. 474 - 480
Hauptverfasser: Zhang, Zhaohui, Zuo, Zengyang, Li, Zhi, Yin, Yuguo, Chen, Yan, Zhang, Tianyao, Zhao, Xiaoyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tokyo Fuji Technology Press Co. Ltd 01.05.2023
Schlagworte:
Accuracy
Algorithms
Classification
Datasets
Deep learning
Identification
Identification methods
Informatics
Kernels
Machine vision
Methods
Object recognition
Physical work
Vision systems
Wheat
ISSN:1343-0130, 1883-8014
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Abstract 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.
AbstractList 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.
Author Zhang, Zhaohui
Zuo, Zengyang
Zhang, Tianyao
Yin, Yuguo
Zhao, Xiaoyan
Li, Zhi
Chen, Yan
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Snippet 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...
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SubjectTerms Accuracy
Algorithms
Classification
Datasets
Deep learning
Identification
Identification methods
Informatics
Kernels
Machine vision
Methods
Object recognition
Physical work
Vision systems
Wheat
Title Real-Time Wheat Unsound Kernel Classification Detection Based on Improved YOLOv5
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