Mechanical Engineering of Biosystems Department, Ilam University, Ilam, Iran.
Department of Environmental Health, Faculty of Health, Ilam University of Medical Science, Ilam, Iran.
Sci Rep. 2022 Aug 11;12(1):13697. doi: 10.1038/s41598-022-18036-8.
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 µg.m. 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 × 10. The results showed the high ability of the developed machine vision system for estimating dust concentration with high speed and accuracy.
扬尘现象是主要的环境问题之一，它会对人类健康和经济社会活动产生反作用。在本研究中，我们开发了一种基于图像处理的新型算法，用于估算粉尘浓度。我们搭建了一个实验装置来产生不同浓度值（0 到 2750μg/m³）的空气传播粉尘。通过图像处理技术获取并分析不同粉尘浓度值的图像。我们从不同颜色空间中提取不同的颜色和纹理特征。然后使用回归方法从提取的特征中开发单变量和多变量模型。总共得到了 285 个单变量模型，并通过比较从中选择有效的特征。最佳的单变量模型的预测准确率为 91%。我们还使用这些特征进行多变量建模，并选择了预测准确率为 100%、均方误差为 1.44×10⁻³的最佳模型。结果表明，所开发的机器视觉系统具有高速、高精度估算粉尘浓度的能力。
