期刊文献+

基于改进VGG16网络模型的花卉分类 被引量:8

Flower Classification Based on Improved VGG16 Network Model
下载PDF
导出
摘要 为进一步提高花卉分类的准确率,在对现有的VGG16网络模型进行研究的基础上,提出一种基于视觉注意力机制的网络模型.将SE视觉注意力模块嵌入到VGG16网络模型中,实现了对花卉显著性区域特征的提取;为有效防止梯度爆炸及梯度消失,加快网络的训练和收敛的速度,在各卷积层后加入BN层;采用多损失函数融合的方式对新模型进行训练.新模型能有效提取花卉的花蕊、花瓣等显著性区域,放大了花卉的类间距离,缩小了类内距离,加快了网络的收敛,进一步提高了花卉分类的准确率.实验结果表明,新模型在Oxford-102数据集上的分类准确率比未引入注意力前有较大提高,与参考文献相比,分类准确率也有较大的提高. To further improve the accuracy of flower classification,this study proposes a network model based on visual attention mechanism after the research on the VGG16 network model.Squeeze-and-excitation(SE)attention is embedded in the VGG16 network model to extract salient region features of flowers.BN layer is added following the convolutional layer to effectively prevent gradient explosion and gradient disappearance and to speed up the training and convergence of the network.Multi-loss function fusion is utilized to train the new model.The new model can effectively extract salient regions such as stamens and petals.It can enlarge the distance between and within classes and accelerate the convergence,further improving the classification accuracy.Experimental results show that the new model advances in the classification accuracy of the Oxford-102 dataset after the introduction of the attention mechanism and outstrips the current reference.
作者 侯向宁 刘华春 侯宛贞 HOU Xiang-Ning;LIU Hua-Chun;HOU Wan-Zhen(The Engineering&Technical College of Chengdu University of Technology,Leshan 614000,China;School of Computer and Software Engineering,Xihua University,Chengdu 610039,China)
出处 《计算机系统应用》 2022年第7期172-178,共7页 Computer Systems & Applications
基金 四川省教育厅重点项目(18ZA0077) 成都理工大学工程技术学院基金(C122020006)。
关键词 VGG16 注意力机制 SE模块 损失函数 深度学习 图像分类 VGG16 attention mechanism squeeze-and-excitation(SE)module loss function deep learning image classification
  • 相关文献

参考文献7

二级参考文献50

共引文献138

同被引文献68

引证文献8

二级引证文献9

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部