稠密融合卷积神经网络的多模态地基云状分类  

Multi-modal ground-based cloud classification based on dense fusion convolutional neural network

作　　者：刘爽[1] 许依琳 张重[1] Liu Shuang;Xu Yilin;Zhang Zhong(College of Electronic and Communication Engineering,Tianjin Normal University,Tianjin 300387,China)

机构地区：[1]天津师范大学电子与通信工程学院,天津300387

出　　处：《电子测量技术》2021年第20期157-161,共5页Electronic Measurement Technology

基　　金：国家自然科学基金(62171321);天津市自然科学基金重点项目(20JCZDJC00180,19JCZDJC31500);天津师范大学教学改革项目(重点项目)(JGZD01220014)资助。

摘　　要：为了解决现有的地基云状分类方法对多模态信息利用不充分的问题,有效融合地基云样本的视觉特征与多模态特征,提出稠密融合卷积神经网络的多模态地基云状分类方法。稠密融合卷积神经网络采用卷积神经网络作为视觉子网络提取地基云图像的视觉特征,采用多模态子网络提取多模态特征,在网络内部加入了5个稠密融合模块,用于充分融合视觉特征与多模态特征,稠密融合模块在不改变原有网络结构的同时,能够独立地加到入子网络中,具有较大的灵活性。在多模态地基云公开数据集MGCD上的实验达到了89.14%的分类精度,验证了所提出的稠密融合卷积神经网络在地基云状分类任务中的有效性。In order to solve the issue that the existing ground-based cloud classification methods can not make full use of multi-modal information,we propose the dense fusion convolutional neural network(DFCNN)for multi-modal groundbased cloud classification to effectively integrate the visual features and the multi-modal features of ground-based cloud samples.The DFCNN utilizes convolution neural network as the visual subnet to extract visual features and adopts the multi-modal subnet to extract multi-modal features of cloud samples.There are five dense fusion modules(DFM)in the DFCNN and they are employed to fully fuse visual features and multi-modal features.The DFM could be injected into the subnet independently without changing the original network structure,and therefore it possesses great flexibility.The DFCNN achieves the classification accuracy of 89.14%on the public multi-modal ground-based cloud dataset MGCD,which verifies the effectiveness of the proposed DFCNN for the ground-based cloud classification task.

关 键 词：卷积神经网络 多模态地基云状分类 稠密融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]