Asset Details
Do you wish to reserve the book?
基于I_CBAM-DenseNet模型的小麦发育期识别研究
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
基于I_CBAM-DenseNet模型的小麦发育期识别研究
Do you wish to request the book?
基于I_CBAM-DenseNet模型的小麦发育期识别研究
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
基于I_CBAM-DenseNet模型的小麦发育期识别研究
2025
Overview
TP391.4; 针对我国农作物发育期人工观测效率低、识别准确率不高等问题,提出一种基于I_CBAM-DenseNet模型的小麦发育期识别方法.该方法以密集连接卷积网络(DenseNet)为主干提取网络,融入卷积块注意模块CBAM.先将CBAM中的空间注意力模块(SAM)与通道注意力模块(CAM)由传统的串联连接改为并行连接,并将改进的CBAM(I_CBAM)插入到DenseNet最后一个密集网络中,构建一种I_CBAM-DenseNet模型,再选取小麦 7个重要发育时期进行自动识别.为最大化提取小麦的特征信息,将超绿特征(ExG)因子和最大类间方差法(Otsu)相结合对采集到的小麦图像进行分割处理.对比分析了I_CBAM-DenseNet、AlexNet、ResNet、DenseNet、CBAM-DenseNet以及VGG等模型的准确率和损失值的变化.结果表明,采取基于I_CBAM-DenseNet的卷积神经网络建立的模型,准确率达到 99.64%,高于对比模型.
Publisher
南京信息工程大学 自动化学院,南京,210044,南京信息工程大学 大气环境与装备技术协同创新中心,南京,210044,南京信息工程大学 江苏省大数据分析技术重点实验室,南京,210044%中国气象局气象探测中心,北京,100081%南京信息工程大学 自动化学院,南京,210044%南京信息工程大学 自动化学院,南京,210044,中国气象局气象探测中心,北京,100081
We currently cannot retrieve any items related to this title. Kindly check back at a later time.