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吉林化工学院学报, 2023, 40(5): 26-33     https://doi.org/10.16039/j.cnki.cn22-1249.2023.05.006
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RRT algorithm; path planning; target offset; variable step size; cubic B-spline curve 基于改进YOLOv5s的玉米田间杂草检测方法
张 鋆1**,李温温2*
1.吉林化工学院 信息与控制工程学院,吉林 吉林132022; 2.白城师范学院 机械与控制工程学院,吉林 白城137000
Method of Weed Detection in Maize Field based on Improved YOLOv5s
ZHANG Yun1** ,LI Wenwen2*
School of Information and Control Engineering, Jilin Institute of Chemical Technology,Jilin City132022,China;School of Mechanical and Control Engineering, Baicheng Normal University,Baicheng 137000,China
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摘要 

针对玉米田间环境中,对幼苗与杂草的检测存在实时性差以及精度不足的问题,将玉米幼苗及常见的四种伴生杂草作为研究对象,提出一种基于改进YOLOv5s的玉米田间杂草检测方法?以YOLOv5s为基础模型,提出一种DCA注意力模块并嵌入特征提取网络的C3结构中,来强化模型的特征表达能力?在损失函数的计算部分引入EIOU损失函数来衡量模型训练过程中的定位损失,优化模型的收敛速度和定位精度?实验表明,改进的YOLOv5s模型在玉米与伴生杂草数据集上mAP@0.5达到95.7%,mAP@0.5:0.95为81.9%,每秒检测帧数为61帧,满足检测精度以及实时性的要求?

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张 鋆
李温温
关键词:  深度学习  杂草检测  YOLOv5s  损失函数  注意力机制     
Abstract: 

To address the challenges of inadequate real-time detection and limited accuracy in identifying seedlings and weeds within maize field environments, we propose a novel weed detection method based on an enhanced version of YOLOv5s. Our study focuses on maize seedlings and four commonly found associated weeds. Utilizing YOLOv5s, which integrates detection accuracy and speed, as the foundational model, we incorporate the DCA attention module proposed in this study into the C3 structure of the feature extraction network to augment the model's capacity for expressing features.In the loss function calculation, the EIOU loss function is utilized to quantify positioning loss during model training and optimize both convergence speed and positioning accuracy of the model. Experimental results demonstrate that the enhanced YOLOv5s model achieves a 95.7% mAP@0.5 and an 81.9% mAP@0.5:0.95 on the maize and associated weeds dataset, with a detection frame rate of 61 frames per second, meeting both accuracy and real-time requirements.

Key words:  deep learning    weed detection    YOLOv5s    loss function    attention
               出版日期:  2023-05-25      发布日期:  2023-05-25      整期出版日期:  2023-05-25
TP 391.41  
引用本文:    
张 鋆, 李温温. RRT algorithm; path planning; target offset; variable step size; cubic B-spline curve 基于改进YOLOv5s的玉米田间杂草检测方法 [J]. 吉林化工学院学报, 2023, 40(5): 26-33.
ZHANG Yun , LI Wenwen. Method of Weed Detection in Maize Field based on Improved YOLOv5s . Journal of Jilin Institute of Chemical Technology, 2023, 40(5): 26-33.
链接本文:  
https://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2023.05.006  或          https://xuebao.jlict.edu.cn/CN/Y2023/V40/I5/26
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