Vision-Based PM Concentration Estimation With Natural Scene Statistical Analysis.
IEEE Trans. Artif. Intell.(2024)
摘要
As the primary pollutant in China's urban atmosphere, PM
$_{2.5}$
poses a great threat to the health of residents and ecological stability. Efficient and effective PM
$_{2.5}$
concentration monitoring is essential. Nonetheless, the popular devices for PM
$_{2.5}$
monitoring are developed based on two standards: the micro-oscillation balance method and the
$\beta$
-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM
$_{2.5}$
concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the ‘haze’ formed by PM
$_{2.5}$
. Moreover, we develop a lightweight deep belief network (DBN)-deep neural network (DNN)-based PM
$_{2.5}$
concentration estimation model, which learns the mapping from the designed visual priors to PM
$_{2.5}$
concentrations. Experiments confirm the superiority of our vision-based PM
$_{2.5}$
concentration estimation method by comparison with state-of-the-art photo-based PM
$_{2.5}$
monitoring methods.
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关键词
Natural scene statistical analysis,PM $_{2.5}$ concentration estimation,saturation,structure,vision
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