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Halation-Based Nighttime PM2.5 Estimation
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AI
DOI：10.1109/JSEN.2025.3525712.png)
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Abstract
En 中文
Camera-based inference techniques can be used to estimate PM(2.5)concentrations in air based on the aggregate effects of particles on light scattering and absorption. These techniques can be spatially fine-grained, operate in real time, and substantially improve accuracy compared with particle counting sensors. However, existing camera-based techniques fail at night, when pollution exposure and production remain important. We describe the first vision-based technique for nighttime PM2.5 concentration estimation. The design approach differs substantially from that of daytime systems because the primary source of daytime information, the progression of color toward airlight color with increasing depth, is much less useful at night and the primary source of nighttime information, the glowing halation regions around artificial light sources, is insignificant during the day. We describe a nighttime pollution estimation technique that builds upon novel illumination map (IM) feature. We describe an IM-based dual-channel squeeze-and-excitation convolutional neural network (DSECNet) is to estimate PM(2.5 )concentrations. This method is evaluated on real-world data and images and outperforms the most advanced related existing (daytime) haze estimation methods, achieving a mean absolute error (MAE) of 8.65 mu g/m(3) , which is 16.99% lower than the state-of-the-art baseline method. To the best of the authors' knowledge, this is the first vision-based nighttime nighttime PM(2.5)estimation method.
Keywords:
Estimation
Monitoring
Sensors
Atmospheric modeling
Air quality
Light sources
Artificial light
Feature extraction
Channel estimation
Scattering
Air quality estimation
dataset
glow effect
nighttime image
PM25 concentration
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W
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