The technique for estimating the concentration of fine particulate matter(PM2.5)based on visual cues relies on assessing its concentration by considering the overall effect of suspended fine particles on light scattering and absorption during imaging.These technologies demonstrate robust generalizability and real-time detection capabilities across large-scale areas.Existing studies depend on daytime scenes with uniform and sufficient atmospheric light,limiting their applicability to nighttime scenario with insufficient atmospheric light and uneven illumination.This paper introduces the pioneering vision-based nighttime estimation method of fine particulate matter concentration,which captures the intensity distribution of an artificial light source in various scattering directions through image processing,and utilizes the feature to correlate with fine particulate matter concentration.The proposed method innovatively leverages the artificial light source and its surrounding glowing area as the main source of nighttime haze information.Since areas dominated by natural lighting typically appear black at night,the conventional basis for daytime haze estimation(i.e.,pixel value approaching the color of"atmospheric-light/sky"as the depth of field increases),is barely useful at night.This method outperforms existing daytime haze estimation methods,achieving a mean absolute error(MAE)of 6.187 μg/m3 and a correlation coefficient(R2)of 0.857.Compared to the popular end-to-end neural network method,it shows a relative improvement of 20.69%in MAE and 13.36%in R2.
关键词
空气质量估计/计算机视觉/细颗粒物/光晕/夜间图像
Key words
air quality estimation/computer vision/fine particulate matter/glow/nighttime image
维普
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