AQMon: A Fine-Grained Air Quality Monitoring System based on UAV Images for Smart Cities

Abstract

Air quality monitoring is important to the green development of smart cities. Several technical challenges exist for intelligent, high-precision monitoring, such as computing overhead, area division, and monitoring granularity. In this paper, we propose a fine-grained air quality monitoring system based on visual inspection analysis embedded in unmanned aerial vehicle (UAV), referred to as AQMon. This system employs a lightweight neural network to obtain an accurate estimate of atmospheric transmittance in visual information while reducing computation and transmission overhead. Considering that air quality is affected by multiple factors, we design a dynamic fitting approach to model the relationship between scattering coefficients and PM2.5 concentration in real time. The proposed system is evaluated using public datasets and the results show that AQMon outperforms four existing methods with a processing time of 13.8ms.