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Image-based dust quantification: a novel approach using texture and color features.

Jiangjiang Yin¹, Shaofeng Wang¹, Liwei Shi¹

¹School of Resources and Safety Engineering, Central South University, Changsha, Hunan, 410083, China.

Environmental Pollution (Barking, Essex : 1987)

|April 27, 2025

Summary

This summary is machine-generated.

A new image-based method accurately quantifies airborne dust concentration using texture and color features. This approach offers a promising alternative for real-time, high-precision dust monitoring in mining environments.

Keywords:

Color model Concentration quantification Dust pollution Image features Texture analysis

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Area of Science:

Environmental Science
Industrial Hygiene
Image Processing

Background:

Airborne dust in mining environments presents significant health and environmental risks.
Traditional dust monitoring methods lack real-time detection and spatial accuracy.
Fine particulate matter necessitates advanced monitoring solutions.

Purpose of the Study:

To develop and validate an image-based methodology for quantifying airborne dust concentration.
To integrate texture and color features for enhanced dust detection precision.
To assess the feasibility of real-time, high-precision dust monitoring.

Main Methods:

Utilized a self-designed experimental setup simulating underground mining conditions.
Acquired high-resolution images of dust dispersion under controlled conditions.
Extracted texture (Entropy) and color (Standard Deviation) features across multiple color models.

Main Results:

Demonstrated a strong correlation between image features and dust concentration (R² = 0.90).
Achieved expanded measurement ranges for dust quantification.
Comparative analysis showed high accuracy and computational efficiency against existing methods.

Conclusions:

The image-based methodology offers a viable solution for accurate dust concentration quantification.
The approach shows potential for real-time, high-precision dust monitoring in industrial settings.
This work provides a foundation for advanced image-based dust monitoring applications.