  Environmental air quality affects people's life, obtaining real-time and
accurate environmental air quality 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 detector at
specific monitoring points in cities and timing sampling analysis, which is
easy to be restricted by time and space factors. Some air quality measurement
algorithms related to deep learning mostly adopt a single convolutional neural
network to train the whole image, which will ignore the difference of different
parts of the image. In this paper, we propose a method for air quality
measurement based on double-channel convolutional neural network ensemble
learning to solve the problem of feature extraction for different parts of
environmental images. Our method mainly includes two aspects: ensemble learning
of double-channel convolutional neural network and self-learning weighted
feature fusion. We constructed a double-channel convolutional neural network,
used each channel to train different parts of the environment images for
feature extraction. We propose a feature weight self-learning method, which
weights and concatenates the extracted feature vectors, and uses the fused
feature vectors to measure air quality. Our method can be applied to the two
tasks of air quality grade measurement and air quality index (AQI) measurement.
Moreover, we build an environmental image dataset of random time and location
condition. The experiments show that our method can achieve nearly 82% accuracy
and a small mean absolute error (MAE) on our test dataset. At the same time,
through comparative experiment, we proved that our proposed method gained
considerable improvement in performance compared with single channel
convolutional neural network air quality measurements.
