Dust pollution poses a persistent challenge within mining processes, giving rise to a spectrum of
concerns, including occupational diseases, mechanical degradation, diminished visibility, and the
potential for dust explosion incidents. Existing dust detection and monitoring methodologies face
limitations, including insufficient measurement accuracy and intricate processing procedures.
Consequently, a comprehensive identification and evaluation system rooted in image processing
technology is proposed to facilitate real-time measurements of dust pollution in mining worksites.
The study primarily employs grey scale average and fractal dimensions to characterise particle
features evident in collected dust images. As a result, the data processing platform can dynamically
process data and present dust pollution conditions. Notably, this research integrates the monitoring
and assessment of dust pollution with the systematic adjustment of mining parameters, ventilation
parameters, and strategies for dust reduction. Through statistical analysis of dust concentration and
particle size during mining processes, rock properties such as components and cuttability can inform
mining parameters. This information, in turn, guides detailed excavation strategies, including
cutterhead speeds, torque, and excavation speed.
Similarly, analysing dust characteristics derived from images is a valuable resource for gaining
deeper insights into the efficacy of implemented ventilation strategies. By harnessing vision-based
information detailing dust concentration and distribution, a wealth of direct guidance becomes
available for refining ventilation parameters. This encompasses crucial aspects such as the selection
of appropriate ventilation systems tailored to the specific mining environment and the optimisation of
operational conditions for ventilation fans. Additionally, the detailed information derived from the
visual data allows for strategic decisions on factors like the spatial arrangement of ventilation outlets
and the calibration of airflow rates.
Moreover, precise choices for dust reduction measures and parameters can be made based on the
prevailing dust conditions. Upon real-time monitoring of dust pollution using the proposed method,
corresponding measures such as spraying and foaming can be promptly implemented. Crucial
parameters, such as spray angles and volume, are integral to these application processes. In
essence, the proposed method assumes a pivotal role in not only dust monitoring but also the
formulation of preventative and control measures within mining processes.