Halation-Based Nighttime PM2.5 Estimation

Abstract

Camera-based inference techniques can be used to estimate $\text {PM}_{{2.5}}$ 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 PM2.5 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 $8.65~\mu \text { g/m}^{{3}}$ , 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 PM2.5 estimation method.