| Summary: | PM<sub>2.5</sub> in the atmosphere causes severe air pollution and dramatically affects the normal production and lives of residents. The real-time monitoring of PM<sub>2.5</sub> concentrations has important practical significance for the construction of ecological civilization. The mainstream PM<sub>2.5</sub> concentration prediction algorithms based on electrochemical sensors have some disadvantages, such as high economic cost, high labor cost, time delay, and more. To this end, we propose a simple and effective PM<sub>2.5</sub> concentration prediction algorithm based on image perception. Specifically, the proposed method develops a natural scene statistical prior to estimating the saturation loss caused by the ’haze’ formed by PM<sub>2.5</sub>. After extracting the prior features, this paper uses the feedforward neural network to achieve the mapping function from the proposed prior features to the PM<sub>2.5</sub> concentration values. Experiments constructed on the public Air Quality Image Dataset (AQID) show the superiority of our proposed PM<sub>2.5</sub> concentration measurement method compared to state-of-the-art related PM<sub>2.5</sub> concentration monitoring methods.
|
|---|