Fine-Grained PM2.5 Detection Method based on Crowdsensing
Pengqi Hao, Min Ji Yang, Shibo Gao, Kunning Sun, Dan Tao · 2020
In this paper, we propose a fine-grained PM2.5 detection method based on crowdsensing technology. Firstly, we perform dark channel processing on sky images which have been collected by mobile users through APP. Secondly, we adopt a train model based on Tensorflow and Keras architecture, and utilize neural network to implement PM2.5 feature extraction and detection. Finally, a series of experiments have been carried out based on a large-scale real dataset to verify the performance of our proposed detection method.