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An improved YOLOv5 method for clam seedlings biological feature detection under the microscope

文献类型： 外文期刊

作者： Zhao, Yue 1 ; Fan, Jiangchuan 1 ; Jiang, Yongcheng 5 ; Guo, Xinyu 2 ; Gou, Wenbo 2 ; Wen, Weiliang 2 ; Lu, Xianju 2 ; Xu, Wenkai 1 ; Zhao, Chunjiang 1 ; Chen, Liping 1 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.China Natl Engn Res Ctr Informat Technol Agr NERCI, Beijing 100097, Peoples R China

4.China Agr Univ, Natl Innovat Ctr Digital Fishery, Beijing 100083, Peoples R China

5.Tianjin Agr Univ, Coll Engn & Technol, Tianjin 300384, Peoples R China

关键词： Deep learning; Object detection; YOLOv5; Clam; Attention mechanism

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 214 卷

页码：

收录情况： SCI

摘要： In order to monitor the early growth status of clam seedlings and meet the demands of precision seedling cultivation in a factory setting, this study improves the YOLOv5s model and proposes a high-precision and lightweight method for detecting biological features of clam seedlings. Firstly, dataset of clam seedlings under the microscope was constructed. Secondly, we established a clam seedling detection model based on four different attention mechanisms and analyzed the differences in the features focused on by different attention mechanisms in clam seedling detection. The accuracy of the anchors was optimized using the K-means++ al-gorithm, and the Soft-NMS method was employed to address the issue of missed detections caused by dense stacking of clam seedlings. The proposed approach has the optimal comprehensive performance by comparing various lightweight networks. The results show that the AP-health, AP-death and mAP of this approach are 98.15%, 93.87% and 96.01%, which can meet the standard for high precision. With a transmission speed of 86 FPS, an average response time of 11.54 ms, and a model size of 27.59 MB, the approach satisfies the requirements for efficiency and portability. Finally, a biological feature detection software of clam seedlings was developed, which can automatically calculate phenotypic information such as the number of clam seedlings, survival rate, and average size. This paper provides a basis for real-time and accurate assessment of clam seedlings biological features while offering technical support for the automation and intelligence of clam seedlings production in factories.

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