|本期目录/Table of Contents|

[1]叶琪,王丽芬,马明涛,等.基于改进YOLO v8的草莓病害检测方法[J].江苏农业科学,2024,52(20):250-259.
　Ye Qi,et al.Strawberry disease detection method based on improved YOLO v8[J].Jiangsu Agricultural Sciences,2024,52(20):250-259.
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基于改进YOLO v8的草莓病害检测方法(PDF)
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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:
250-259
栏目:
病虫害智能检测
出版日期:
2024-10-20

文章信息/Info

Title:
Strawberry disease detection method based on improved YOLO v8
作者:
叶琪1; 2;  王丽芬2;  马明涛2;  赵鑫1; 2;  段必冲1; 2
1.吉林化工学院信息与控制工程学院，吉林吉林 132022； 2.吉林农业科技学院电气与信息工程学院，吉林吉林 132101
Author(s):
Ye Qi; et al
关键词:
病害检测; YOLO v8; 注意力机制; Slim-Neck; MPDIoU
Keywords:
-
分类号:
S126；TP391.41
DOI:
-
文献标志码:
A
摘要:
针对自然条件下草莓病害检测难度大、人工检测效率低下、传统计算机检测方式步骤繁琐、检测精度差以及模型的参数量与计算量大的问题，构建一种基于改进YOLO v8的草莓病害检测模型。该模型使用Slim-Neck结构代替原YOLO v8网络的颈部(Neck)结构以降低深度可分离卷积特征提取和融合能力差的缺陷对模型造成的负面影响，在降低模型参数量和计算量的同时不会损失检测的准确度，并且该结构能使模型更好地应用于复杂的草莓种植环境。模型还引入了通道注意力和空间注意力机制(CBAM)以提高病害特征的提取能力同时忽略图片中不相关的信息。最后模型将YOLO v8中的边界框损失函数替换为MPDIoU以提升检测和目标定位的能力。结果表明，本模型在一个含有7类草莓病害的开源数据集可以实现96.5%的平均精度(mAP)，同时仅有2.9 M参数量和 7.4 GFLOPs 值，相比于原始YOLO v8n、YOLO v7-tiny、YOLO v6n和YOLO v5s模型的mAP分别提升1.2、1.9、3.7和2.5百分点。改进后的模型具有更高的检测精度和更小的参数量与计算量，可为实际草莓种植环境下的病害检测提供参考。
Abstract:
-

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备注/Memo

备注/Memo:
收稿日期：2023-11-02
基金项目：吉林省科技发展计划(编号：20230201073GX)；吉林省重点新兴交叉学科“数字农业”课题。
作者简介：叶琪(2000—)，男，福建泉州人，硕士研究生，研究方向为计算机视觉。E-mail：1564633612@qq.com。
通信作者：王丽芬，硕士，副教授，主要从事计算机图形学、软件工程研究，E-mail：306923482@qq.com；马明涛，博士，教授，主要从事电子电路和信号的检测、传输研究，E-mail：35172318@qq.com。
更新日期/Last Update: 2024-10-20