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Detection method of wheat spike improved YOLOv5s based on the attention mechanism

文献类型： 外文期刊

作者： Zang, Hecang 1 ; Wang, Yanjing 3 ; Ru, Linyuan 4 ; Zhou, Meng 1 ; Chen, Dandan 1 ; Zhao, Qing 1 ; Zhang, Jie 1 ; Li, Guoqiang 1 ; Zheng, Guoqing 1 ;

作者机构： 1.Henan Acad Agr Sci, Inst Agr Econ & Informat, Zhengzhou, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Huang Huai Hai Smart Agr Technol, Zhengzhou, Peoples R China

3.Zhengzhou Normal Univ, Coll Life Sci, Zhengzhou, Peoples R China

4.Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang, Peoples R China

关键词： wheat; spike number detection; attention mechanism; deep learning; YOLOv5s

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely and accurate acquisition of wheat spike number is of great practical significance for yield prediction. In actual production; the method of using an artificial field survey to count wheat spikes is time-consuming and labor-intensive. Therefore, this paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of the wheat spikes. This method introduces an efficient channel attention module (ECA) in the C3 module of the backbone structure of the YOLOv5s network model; at the same time, the global attention mechanism module (GAM) is inserted between the neck structure and the head structure; the attention mechanism can be more Effectively extract feature information and suppress useless information. The result shows that the accuracy of the improved YOLOv5s model reached 71.61% in the task of wheat spike number, which was 4.95% higher than that of the standard YOLOv5s model and had higher counting accuracy. The improved YOLOv5s and YOLOv5m have similar parameters, while RMSE and MEA are reduced by 7.62 and 6.47, respectively, and the performance is better than YOLOv5l. Therefore, the improved YOLOv5s method improves its applicability in complex field environments and provides a technical reference for the automatic identification of wheat spike numbers and yield estimation.

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