Image processing techniques for dust monitoring in industry: Controlled-environment experiments and feature-based concentration mapping

Abstract Dust pollution in industrial environments should be closely monitored for health and process control. Traditional measurement methods often require intrusive sensors and lack continuous real-time capability. A novel non-intrusive, image-based method was proposed for real-time dust concentration estimation. Using a controlled experimental setup with a high-concentration dust generator, images of dust-laden air across a wide concentration range (10 –1000 mg/m³) were captured. For each image, the color and texture features were extracted as predictors of dust concentration. Then the polynomial regression was used to correlate these image-derived features with actual dust concentrations measured by standard instrumentation. The analysis revealed strong nonlinear relationships: the grayscale mean intensity correlates with dust concentration ( R ² = 0.79), and selected texture features yield even higher correlation ( R ² > 0.82). These image-derived metrics effectively capture the scattering and attenuation of light caused by suspended dust, serving as reliable proxies for particulate mass. Combining multiple image features into a composite regression model further improved estimation accuracy. These findings demonstrate that camera-based image analysis can reliably estimate dust concentration in real time, offering a low-cost, non-intrusive alternative to conventional sensors. The approach could facilitate automated calibration of dust-generation systems and enable continuous air quality monitoring in industrial settings.