Abstract

The dust phenomenon is one of the main environmental problems that it reversely affects human health and economical and social activities. In the present research, a novel algorithm has been developed based on image processing to estimate dust concentration. An experimental setup was implemented to create airborne dust with different concentration values from 0 to 2750 mu g.m(-3). The images of the different dust concentration values were acquired and analyzed by image processing technique. Different color and texture features were extracted from various color spaces. The extracted features were used to develop single and multivariable models by regression method. Totally 285 single variable models were obtained and compared to select efficient features among them. The best single variable model had a predictive accuracy of 91. The features were used for multivariable modeling and the best model was selected with a predictive accuracy of 100 and a mean squared error of 1.44 x 10(-23). The results showed the high ability of the developed machine vision system for estimating dust concentration with high speed and accuracy.