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Cabbage Transplantation State Recognition Model Based on Modified YOLOv5-GFD

文献类型： 外文期刊

作者： Sun, Xiang 1 ; Miao, Yisheng 1 ; Wu, Xiaoyan 2 ; Wang, Yuansheng 1 ; Li, Qingxue 1 ; Zhu, Huaji 1 ; Wu, Huarui 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

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

3.Minist Agr & Rural Affairs, Key Lab Digital Village Technol, Beijing 100125, Peoples R China

4.Guangxi Univ, Sch Comp & Elect Informat, Nanning 530000, Peoples R China

关键词： the state of cabbage transplantation; target detection; deep separable convolution; YOLOv5s

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2024 年 14 卷 4 期

页码：

收录情况： SCI

摘要： To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these challenges, this study explores a lightweight yet efficient algorithm for recognizing cabbage transplantation states in natural settings. Initially, FasterNet is integrated as the backbone network in the YOLOv5s model, aiming to expedite convergence speed and bolster feature extraction capabilities. Secondly, the introduction of the GAM attention mechanism enhances the algorithm's focus on cabbage seedlings. EIoU loss is incorporated to improve both network convergence speed and localization precision. Lastly, the model incorporates deformable convolution DCNV3, which further optimizes model parameters and attains a superior balance between accuracy and speed. Upon testing the refined YOLOv5s target detection algorithm, improvements were evident. When compared to the original model, the mean average precision (mAP) rose by 3.5 percentage points, recall increased by 1.7 percentage points, and detection speed witnessed an impressive boost of 52 FPS. This enhanced algorithm not only reduces model complexity but also elevates network performance. The method is expected to streamline transplantation quality measurements, minimize time and labor inputs, and elevate field transplantation quality surveys' automation levels.

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