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《江苏农业科学》[ISSN:1002-1302/CN:32-1214/S]

卷:

第52卷

期数:

2024年第20期

页码:

165-172

栏目:

杂草智能检测

出版日期:

2024-10-20

文章信息/Info

Title:

Weed detection network in maize field based on improved YOLO v8

作者:

亢洁;  代鑫;  刘文波;  徐婷;  夏宇

陕西科技大学电气与控制工程学院，陕西西安 710021

Author(s):

Kang Jie; et al

关键词:

玉米田; 杂草; 目标检测; YOLO v8n; EMA注意力机制

Keywords:

-

分类号:

S126；TP391.41

DOI:

-

文献标志码:

A

摘要:

针对3~5叶期玉米田间伴生杂草目标尺度小、玉米叶片遮挡严重、田间自然环境复杂等导致检测精度不高的问题，提出了一种基于改进YOLO v8n的玉米田间杂草检测算法。首先下载涵盖了黑麦草、芥菜、甘菊、藜麦等常见伴生杂草和玉米幼苗的图像，对图像进行翻转等数据增强方式增加样本多样性，提升模型识别和泛化能力。其次在YOLO v8n网络基础上，重新构建了轻量级跨尺度特征融合网络，增强模型多尺度特征融合能力，并输出一个针对小目标杂草的预测层，提升网络的检测精度。最后，在4个目标检测头前嵌入高效多尺度注意力机制EMA，使得检测头更加专注于目标区域。试验结果表明，本模型的平均精度均值提升了2.4百分点、杂草的平均精度提升了5.1百分点，模型内存用量和参数量分别减小了22.6%和26.0%；本模型与SSD-MobileNet v2、Efficientdet-D0及YOLO系列目标检测模型相比，平均精度均值至少提升了1.8百分点、识别杂草的平均精度至少提升了4.6百分点，并且模型内存用量和参数量都处在较低水平。本研究提出的玉米田间杂草检测模型在降低了模型内存用量和参数量的同时提高了检测精度，可为精准除草设备提供技术支持。

Abstract:

-

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备注/Memo

备注/Memo:

收稿日期：2024-03-25
基金项目：国家自然科学基金(编号：62203285)；陕西省自然科学基础研究计划(编号：2022JQ-181)；西安市科技计划(编号：23NYGG0070)。
作者简介：亢洁(1973—)，女，陕西渭南人，博士，副教授，硕士生导师，主要从事机器视觉、智慧农业方面的研究。E-mail：kangjie@sust.edu.cn。

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更新日期/Last Update: 2024-10-20