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

Chen, Xiaoguang; Ya-ru, LI; Li, Dongyue

doi:10.2991/aiie-16.2016.59

Cited by 6 publications

(3 citation statements)

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“…At the same time, we introduce the mean deviation rate (MDR) as another evaluation criteria of index. The formula of MDR is as equation (7). Where is the ith a sample air quality index predicted value, is the ith a sample air quality index of true value.…”

Section: Test Methods and Evaluation Criteriamentioning

confidence: 99%

“…At present, the existing air quality measurement methods related to image or deep learning are mainly divided into two types: method based on traditional image processing or deep learning. The methods which based on traditional image processing [1,7], are use traditional machine learning algorithms for feature extraction, such as edge detection, direction gradient histogram, etc. The extracted features are analyzed and calculated to get air quality measurement values.…”

Section: Introductionmentioning

confidence: 99%

“…Jian Ma [5] combined the dark channel prior theory [6], firstly extracted the dark channel from the image, trained two convolutional neural networks respectively with the original image and the dark channel images, and identified the good and bad air quality of the image in three grades. Xiaoguang Chen et al [7] proposes a traditional image processing algorithms and deep learning combining approach. First, they counted distribution characteristics of image pixel values, statistics the proportion of high brightness points (pixel value > 128) to all pixel points of each image, use edge detector statistics all image the proportion of edge points to all pixels.…”

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

et al. 2019

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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 selflearning 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.

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Section: Test Methods and Evaluation Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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