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Rock blasting evaluation - image recognition method based on deep learning.

Haibao Yi1,2, Aixiang Wu3, Xiliang Zhang4,5

  • 1School of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing, 100083, China. hang_tianfeiji@126.com.

Scientific Reports
|July 4, 2025
PubMed
Summary
This summary is machine-generated.

Image recognition accurately evaluates rock blasting quality, improving efficiency and accuracy over manual methods. This technology aids in optimizing blasting parameters for better rock fragmentation and shovel loading.

Keywords:
Blasting fragmentationBlasting quality evaluationHalf-hole rate of blastingImage recognitionMachine deep learning

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Area of Science:

  • Geotechnical Engineering
  • Mining Engineering
  • Computer Vision

Background:

  • Evaluating rock blasting quality is crucial for geotechnical and mining operations.
  • Traditional manual analysis of blasting fragmentation and half-hole rates is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and validate an image analysis model for assessing rock blasting fragmentation and half-hole rates.
  • To enhance the efficiency and accuracy of blasting quality evaluation in mines.

Main Methods:

  • Developed a blasting effect image analysis and calculation model using machine learning.
  • Implemented an image recognition algorithm for analyzing pre-splitting blasting fragmentation and half-hole rates.
  • Fitted Pearson curve function and proposed a segmented R-R block size distribution correction model.

Main Results:

  • Image recognition achieved a 67.15% half-hole rate, with a 1.49% average error compared to manual statistics (68.16%).
  • Identified an "S" shaped cumulative distribution pattern for mineral rock block sizes.
  • The proposed model accurately characterizes blasting block size distribution.

Conclusions:

  • Image recognition technology offers a reliable and efficient method for evaluating pre-splitting blasting quality.
  • The developed model provides valuable feedback for adjusting blasting parameters and improving on-site blasting outcomes.
  • This approach significantly overcomes the limitations of traditional manual analysis, showing great application potential.