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Large ore detection in blasting piles using LODM.

Xingfan Zhang1,2, Hongdi Jing3, Miao Yu4

  • 1School of Resources and Security Engineering, University of Science and Technology Beijing, Beijing, 100083, China.

Scientific Reports
|October 6, 2025
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Summary
This summary is machine-generated.

This study introduces a novel Large Ore Detection and Measurement (LODM) model for open-pit mines. The LODM model, utilizing Mask R-CNN and ResNet34, accurately identifies ore fragmentations, enhancing mining efficiency.

Keywords:
Deep learningImage segmentationLODMLarge oreMask R-CNN

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Area of Science:

  • Mining Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Efficient ore fragmentation analysis is crucial for optimizing open-pit mine operations.
  • Traditional image segmentation methods struggle with complex ore pile characteristics.

Purpose of the Study:

  • To develop and evaluate a robust Large Ore Detection and Measurement (LODM) model for analyzing blasted ore fragmentation.
  • To improve the accuracy and efficiency of ore size detection in mining.

Main Methods:

  • Proposed a Large Ore Detection and Measurement (LODM) model based on Mask R-CNN.
  • Trained and evaluated the model on the MPBRD1.0 dataset.
  • Compared LODM with K-means, Canny, watershed, and U-Net algorithms.
  • Investigated ResNet34 as a backbone for feature extraction, comparing with ResNet50, ResNet101, and VGG16.

Main Results:

  • The LODM model demonstrated superior performance compared to traditional algorithms and U-Net.
  • The ResNet34 feature extraction network optimized the LODM model's performance.
  • Detection results closely align with actual ore fragmentation conditions.

Conclusions:

  • The proposed LODM model significantly improves the detection of large ore fragmentations in open-pit mines.
  • Utilizing ResNet34 as the backbone enhances the model's detection capabilities.
  • Accurate ore fragmentation measurement aids in optimizing secondary crushing, loading, and transportation processes.