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

Wang, Shaofeng; Yin, Jiangjiang; Liang, Yuntao; Tian, Fuchao

doi:10.1016/j.jclepro.2022.134691

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…Ye et al ( 2022 ) proposed a visual measurement algorithm of dust concentration based on the calculation of image transmittance characteristic value. Wang et al ( 2022b ) proposed a visual digital monitoring system for dust pollution assessment and monitoring based on the improved gray average (IGSA) and fractal dimension (FD) theory. Miao et al ( 2023 ) developed a hidden danger analysis and risk early warning system for coal mines by using data mining technology.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal evolution of mine dust research: visual knowledge mapping analysis in Web of Science from 2001 to 2021

Zeng

Jiang

2023

Environ Sci Pollut Res

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Dust pollution control is the basic guarantee of mine safety production, which has been widely concerned by scholars. Based on a total of 1786 publications collected by the Web of Science Core Collection (WOSCC) from 2001 to 2021, this paper analyzes the spatial–temporal distribution characteristics, hot topics, and frontier trends of the international mine dust field during the past 20 years by using Citespace and VOSviewer knowledge graph technology. The research shows that the study of mine dust can be divided into three stages: initial period (2001 ~ 2008), stable transition period (2009 ~ 2016), and boom period (2017 ~ 2021). The journals and disciplines which belong to mine dust research mainly focus on environmental science and engineering technology. A stable core group of authors and institutions have been preliminarily formed in the dust research field. The main themes of the study contained the whole process of mine dust generation, transport, prevention, and control, as well as the consequences of disaster. At present, the hot research fields mainly focus on mine dust particle pollution, multi-stage dust prevention, and emission reduction technologies, and mine occupational protection, monitoring, and early warning. In the future, the research should focus on the mechanism of dust production and transportation, the theory of efficient prevention and control, the technology and equipment of precise prevention and control of dust, and the high-precision monitoring and early warning of dust concentration. Future research should be concerned with dust control in underground mines and deep concave open-pit mines with complicated and treacherous environments, and strengthen research institutions, interdisciplinary cooperation, and interaction so as to promote the integration and application of mine dust and automation, information, and intelligent technology.

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Section: Introductionmentioning

confidence: 99%

Spatial and temporal evolution of mine dust research: visual knowledge mapping analysis in Web of Science from 2001 to 2021

Zeng

Jiang

2023

Environ Sci Pollut Res

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Integrating image processing and deep learning for effective analysis and classification of dust pollution in mining processes

Yin,

Lei,

Fan

et al. 2023

Int J Coal Sci Technol

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A comprehensive evaluation method is proposed to analyze dust pollution generated in the production process of mines. The method employs an optimized image-processing and deep learning framework to characterize the gray and fractal features in dust images. The research reveals both linear and logarithmic correlations between the gray features, fractal dimension, and dust mass, while employing Chauvenel criteria and arithmetic averaging to minimize data discreteness. An integrated hazardous index is developed, including a logarithmic correlation between the index and dust mass, and a four-category dataset is subsequently prepared for the deep learning framework. Based on the range of the hazardous index, the dust images are divided into four categories. Subsequently, a dust risk classification system is established using the deep learning model, which exhibits a high degree of performance after the training process. Notably, the model achieves a testing accuracy of 95.3%, indicating its effectiveness in classifying different levels of dust pollution, and the precision, recall, and F1-score of the system confirm its reliability in analyzing dust pollution. Overall, the proposed method provides a reliable and efficient way to monitor and analyze dust pollution in mines.

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Deep learning-based object detection for visible dust and prevention measures on construction sites

Wang,

Yao,

Yang

et al. 2023

Developments in the Built Environment

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

Cited by 7 publications

References 41 publications

Spatial and temporal evolution of mine dust research: visual knowledge mapping analysis in Web of Science from 2001 to 2021

Spatial and temporal evolution of mine dust research: visual knowledge mapping analysis in Web of Science from 2001 to 2021

Integrating image processing and deep learning for effective analysis and classification of dust pollution in mining processes

Deep learning-based object detection for visible dust and prevention measures on construction sites

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