基于改进Swin-Unet的小麦条锈病分割方法
Segmentation Method for Wheat Stripe Rust Based on Improved Swin-Unet
臧贺藏 1任帅 2王从胜 1王盛威 3赵瑞玲 4陈丹丹 1赵晴 1张杰 1郑国清 1李国强1
作者信息
- 1. 河南省农业科学院农业信息技术研究所/农业农村部黄淮海智慧农业技术重点实验室,河南 郑州 450002
- 2. 新乡市农业科学院,河南 新乡 453600
- 3. 中国农业科学院农业信息研究所,北京 100081
- 4. 新乡工程学院,河南 新乡 453706
- 折叠
摘要
条锈病是影响小麦产量及粮食安全的重要因素,条锈病图像的精准分割是实现计算机辅助精准防治的重要基础.针对小麦条锈病图像中病斑形态复杂、病斑与非病斑之间边界模糊、分割精度低的问题,本研究提出了一种基于改进Swin-Unet的小麦条锈病图像分割方法,通过在Swin-Unet中引入SENet(Squeeze-and-Excitation Networks)和残差网络(ResNet)模块来增强模型对条锈病特征的表达能力.实验结果表明,改进Swin-Unet对背景、孢子和叶片的查准率分别为 99.24%、82.32%和 94.36%,可以从复杂环境中有效分割出背景、孢子和叶片图像,具有较好的计算机视觉处理能力和分割评估效果.改进Swin-Unet总体分割准确率、平均交并比和均像素准确率分别为 96.88%、84.91%和 90.50%,较Swin-Unet分别提高了 2.84、4.64 个和5.38个百分点;与其他网络模型(U-Net、PSPNet、DeepLabV3+和Swin-Unet)相比,改进Swin-Unet具有最佳分割效果.表明本研究提出的方法可以精准检测和分割小麦条锈病图像,这可为田间复杂环境下小麦条锈病的自动检测和早期预防提供技术支持.
Abstract
Stripe rust is an important factor affecting wheat yield and food security,and accurate seg-mentation of wheat stripe rust images is an important means of computer-aided precision control.A wheat stripe rust image segmentation method based on improved Swin-Unet was proposed in this study to address the prob-lems of complex lesion morphology,blurred boundaries between lesions and non-lesions,and low segmentation accuracy in wheat stripe rust image.The method enhanced the model's ability to express stripe rust features by introducing SENet and ResNet modules into Swin-Unet.The experimental results showed that the improved Swin-Unet had precision rates of 99.24%,82.32%and 94.36%for background,spore and leaf,respectively,and could segment background,spore and leaf images in challenging situations,so it had better computer vi-sion processing and segmentation evaluation effects.The overall segmentation accuracy,average intersection to union ratio and average pixel accuracy of improved Swin-Unet were 96.88%,84.91%and 90.50%,respective-ly,which were 2.84,4.64 and 5.38 percentage points higher than those of Swin-Unet.Compared with other network models such as U-Net、PSPNet、DeepLabV3+and Swin-Unet,the improved Swin-Unet had the best segmentation performance.The method proposed in this study could accurately detect and segment wheat stripe rust features,providing technical support for automatic detection and early prevention of wheat stripe rust in complex field environments.
关键词
小麦条锈病/语义分割/Swin-Unet/注意力机制Key words
Wheat stripe rust/Semantic segmentation/Swin-Unet/Attention mechanism引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据