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Research on coal-rock boundary identification based on the morphological sobel algorithm.

Guohui Chen1, Yilai Wang2, Shengwei Song2

  • 1School of Mechanical Engineering, Heilongjiang University of Science & Technology, Harbin, 150022, China. laochenlaochen@126.com.

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|October 15, 2024
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Summary
This summary is machine-generated.

A new morphological Sobel algorithm enhances coal-rock boundary recognition in mining images. This method significantly reduces identification errors compared to traditional operators, improving visual observation and intelligent mining capabilities.

Keywords:
Boundary recognitionCoal-rock imageMorphologySobel algorithm

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Area of Science:

  • Mining Engineering
  • Image Processing
  • Computer Vision

Background:

  • Coal mining environments produce noisy, low-quality images, hindering visual inspection and intelligent mining.
  • Existing image processing techniques struggle with the poor quality of underground coal mining imagery.

Purpose of the Study:

  • To develop and evaluate a robust algorithm for accurate coal-rock boundary recognition in low-quality mining images.
  • To improve the quality of coal image data for enhanced visual observation and subsequent intelligent mining applications.

Main Methods:

  • Image preprocessing including smoothing and adaptive thresholding to enhance contrast.
  • Application of morphological corrosion theory for feature boundary extraction.
  • Comparative analysis of the morphological Sobel algorithm against Sobel and Canny operators using area error calculations.

Main Results:

  • The morphological Sobel algorithm demonstrated superior coal-rock boundary identification with higher overlap to original image boundaries.
  • The proposed algorithm achieved an identification error area approximately 10% of that produced by the Sobel and Canny operators.
  • Effective identification of coal and rock boundaries from various angles was achieved through monitoring specimens.

Conclusions:

  • The morphological Sobel algorithm offers a significant improvement for coal-rock boundary recognition in challenging underground mining conditions.
  • This advancement facilitates more reliable visual observation and supports the development of intelligent mining systems.
  • The algorithm's accuracy and efficiency make it a valuable tool for monitoring coal and rock interfaces in real-time.