An Efficient Method for Air Quality Evaluation via ANN-based Image Recognition

DOI

10.2991/aiie-16.2016.59How to use a DOI?

Keywords

Air Quality Index (AQI); image recognition; back-propagation neural network

Abstract

In recent years, air pollution problem has been the focus of public attention. In this paper, we proposed an efficient algorithm to evaluate the Air Quality Index (AQI) based on image recognition technology. In offline stage, some distinctive features extracted from the photos which are captured by common digital cameras, and then a prediction model of back-propagation neural network (BPNN) is trained. In online stage, the feature vectors extracted from the images are fed to the trained BPNN model to output the AQI value. Experimental results show that the proposed algorithm can produce the AQI evaluation with a considerable accuracy 93.78%.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoguang Chen
AU  - Yaru Li
AU  - Dongyue Li
PY  - 2016/11
DA  - 2016/11
TI  - An Efficient Method for Air Quality Evaluation via ANN-based Image Recognition
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 253
EP  - 256
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.59
DO  - 10.2991/aiie-16.2016.59
ID  - Chen2016/11
ER  -