Authors: P.V. Cherkasov, D.Yu. Khudonogov, S.A. Kizilov, M.S. Nikitenko

Title of the article: Assessing the impact of airborne dust and humidity in mine workings on machine vision system efficiency

Year: 2026, Issue: 2, Pages: 77-84

Branch of knowledge: 2.8.8. Geotechnology, Mining machines (engineering)

Index UDK: 622.41 : 004.932.2

DOI: 10.26730/1816-4528-2026-2-77-84

Abstract: The implementation of machine vision technology into automated control systems for mining equipment in coal mines faces the challenge of ensuring its operational reliability under the specific conditions of underground mine workings. A key limitation is degraded visibility, primarily influenced by such regulated mine atmosphere parameters as airborne dust concentration, humidity, and illumination levels. This research aims to analyze the impact of these parameters on the quality of light marker recognition by a machine vision system. The article provides a brief analysis of dust generation sources, including its dispersity and concentrations in the workings, as well as humidity conditions created by dust suppression systems. Requirements for lighting systems to ensure the stable operation of digital video camera optics are presented. The experimental work was conducted on a laboratory-scale mock-up of an isolated section, simulating mine atmosphere conditions for dust and humidity. The experiments investigated the correlation between the light marker recognition coefficient and the concentration of suspended particulate matter at high relative humidity. The results demonstrate that within normative limits for dust and humidity, the quality of marker projection recognition using machine vision technology remains high. Consequently, it is established that the primary destabilizing factors for machine vision in underground environments are elevated concentrations of dust and moisture. The obtained results have enabled the definition of boundary conditions for applying machine vision-based optical systems, provided a foundation for developing adaptive image processing algorithms, and confirmed the feasibility of effectively deploying light marker-based machine vision technology under simulated conditions.

Key words: machine vision coal mine airborne dust humidity efficiency light marker recognition

Receiving date: 15.01.2026

Approval date: 15.03.2026

Publication date: 04.06.2026

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