Dust pollution evaluation based on grayscale average and fractal dimension of digital image

Abstract

The automatic and effective identification of mine dust draws growing public concern for its universality and being highly hazardous to health and property. Based on improved methods for improved grayscale average (IGSA) and fractal dimension (FD) theory, a vision-based system that employs digital image processing was proposed to recognize dust particles. The proposed approach primarily includes the following procedures: image matrix generation; pixel gradient calculations; image processing for sharpening, gray transformation, and binarization; IGSA and FD calculations, and visualization accomplishments. Image example results show that FD appears to have an exponential relationship with IGSA as well as dust pollution. Then, a dust pollution-level evaluation method based on the pollution-factor Fp was established and contributed to the division of dust pollution regions. During the experiment, wind speed and hole distance were proved to demonstrate a positive and negative correlation with IGSA, respectively, while little impact was observed on the FD and Fp with the factors. A dust pollution monitoring system that is theoretically viable was developed to form an Industrial Internet of Things with a topological structure. The proposed approach provides a digital image-processing method to integrally and automatically characterize the dust morphology. This approach can achieve dust pollution-level and regional divisions.