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A method of blasted rock image segmentation based on improved watershed algorithm.

Qinpeng Guo1, Yuchen Wang2, Shijiao Yang3

  • 1School of Resources Environment and Safety Engineering, University of South China, Hengyang, 421000, China. 1516682242@qq.com.

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Summary

This study presents an adaptive watershed segmentation algorithm for accurately determining rock fragmentation particle size. The method enhances rock block contour analysis, achieving over 95.65% accuracy in segmenting limestone and granite particles.

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Area of Science:

  • Geotechnical Engineering
  • Image Processing
  • Computational Geology

Background:

  • Accurate particle size analysis of rock fragmentation is crucial for both theoretical understanding and practical applications.
  • Existing methods often struggle with segmentation inaccuracies due to issues like adhesion, stacking, and blurred edges in blasted rock images.

Purpose of the Study:

  • To develop a fast and accurate detection method for particle size of rock fragmentation.
  • To improve the segmentation of blasted rock images by addressing common segmentation challenges.

Main Methods:

  • Introduced the Phansalkar binarization method.
  • Proposed a watershed seed point marking method utilizing rock block contour solidity.
  • Developed an adaptive watershed segmentation algorithm based on rock block shape for blasted rock piles.

Main Results:

  • The algorithm achieved high consistency with manual segmentation for area cumulative distribution.
  • Segmentation accuracy exceeded 95.65% for both limestone and granite rock blocks larger than 100 cm².
  • Effectively reduced incorrect segmentation possibilities in blasted rock images.

Conclusions:

  • The developed algorithm accurately performs seed point marking and watershed segmentation for blasted rock images.
  • The method offers a novel approach for particle segmentation with potential applications in other fields.
  • Demonstrated significant improvements in accuracy and reliability for rock fragmentation analysis.