Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

doi:10.1109/ACCESS.2019.2945805

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
	Wang, Zhenyu 1; Zheng, Wei 1; Song, Chunfeng 2; Zhang, Zhaoxiang 2; Lian, Jie 1; Yue, Shaolong 1; Ji, Senrong 1
刊名	IEEE ACCESS
	2019
卷号	7 页码:145067-145081
关键词	AQI measurement deep learning CNN image recognition.
ISSN号	2169-3536
DOI	10.1109/ACCESS.2019.2945805
通讯作者	Wang, Zhenyu(zywang@ncepu.edu.cn)
英文摘要	Environmental air quality affects people's lives and has a profound guiding significance for the development of social activities. At present, environmental air quality measurement mainly adopts the method that setting air quality detectors at specific monitoring points in cities with fix-time sampling and slow analysis, which is severely restricted by the time and location. To address this problem, recognizing air quality with mobile cameras is a natural idea. Some air quality measurement algorithms related to deep learning mostly adopt a single convolutional neural network to directly train the whole image, which will ignore the difference of each part of the image. In this paper, in order to learn the combined feature extracted from different parts of the environmental image, we propose the double-channel weighted convolutional network (DCWCN) ensemble learning algorithm. This mainly includes two aspects: ensemble learning of DCWCN and self-learning weighted feature fusion. Firstly, we construct a double-channel convolutional neural network, which uses each channel to train different parts of the environment images for feature extraction. Secondly, we propose a feature weights self-learning method, which weights and concatenates the extracted feature vectors to measure the air quality. Moreover, we build an environmental image dataset with random sampling time and locations to evaluate our method. The experiments show that our method can achieve over 87% accuracy on the newly built dataset. At the same time, through comparative experiments, we proved that the proposed method achieves considerable improvement in terms of performance compared with existing CNN based methods.
资助项目	National Natural Science Foundation of China[61976090] ; National Natural Science Foundation of China[61573139] ; Fundamental Research Funds for the Central Universities[2018ZD05]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000498841300001
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/29403]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Wang, Zhenyu
作者单位	1.North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China 2.Chinese Acad Sci CASIA, Inst Automat, NLPR, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zhenyu,Zheng, Wei,Song, Chunfeng,et al. Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning[J]. IEEE ACCESS,2019,7:145067-145081.
APA	Wang, Zhenyu.,Zheng, Wei.,Song, Chunfeng.,Zhang, Zhaoxiang.,Lian, Jie.,...&Ji, Senrong.(2019).Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning.IEEE ACCESS,7,145067-145081.
MLA	Wang, Zhenyu,et al."Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning".IEEE ACCESS 7(2019):145067-145081.