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Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
Wang, Zhenyu1; Zheng, Wei1; Song, Chunfeng2; Zhang, Zhaoxiang2; Lian, Jie1; Yue, Shaolong1; Ji, Senrong1
Source PublicationIEEE ACCESS
ISSN2169-3536
2019
Volume7Pages:145067-145081
Corresponding AuthorWang, Zhenyu(zywang@ncepu.edu.cn)
AbstractEnvironmental 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.
KeywordAQI measurement deep learning CNN image recognition.
DOI10.1109/ACCESS.2019.2945805
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61976090] ; National Natural Science Foundation of China[61573139] ; Fundamental Research Funds for the Central Universities[2018ZD05] ; National Natural Science Foundation of China[61976090] ; National Natural Science Foundation of China[61573139] ; Fundamental Research Funds for the Central Universities[2018ZD05]
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000498841300001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/29404
Collection模式识别实验室
Corresponding AuthorWang, Zhenyu
Affiliation1.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
Recommended Citation
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.
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