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A Sensor Based Waste Rock Detection Method in Copper Mining Under Low Light Environment.

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This study introduces a deep learning algorithm to improve copper mine waste rock detection in low-light conditions. The enhanced detection system boosts sorting accuracy and efficiency, crucial for environmental management in mining.

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Area of Science:

  • Mining Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Copper mining generates significant waste rock, impacting land use and the environment.
  • Intelligent sorting systems using vision sensors are key for efficient waste rock management.
  • Poor lighting conditions in sorting environments hinder vision-based detection accuracy.

Purpose of the Study:

  • To develop a deep learning algorithm for accurate copper mine waste rock detection in low-light environments.
  • To enhance the performance of vision-based detection systems in challenging industrial settings.
  • To improve the efficiency and cost-effectiveness of copper mine waste rock sorting.

Main Methods:

  • Proposed a deep-learning-based algorithm incorporating an Illumination Adaptive Transformer (IAT) module for image preprocessing.
  • Integrated a Local Enhancement-Global Modulation (LEGM) module to improve detection accuracy within the Neck architecture.
  • Optimized the object detection model using MPDIoU loss function to enhance performance.

Main Results:

  • The proposed algorithm achieved a mean Average Precision (mAP@0.5) of 0.957.
  • The algorithm reached an mAP@0.5:0.95 of 0.689, significantly outperforming existing advanced methods.
  • Demonstrated superior performance in detecting copper mine waste rock under low-light conditions.

Conclusions:

  • The developed deep learning algorithm effectively addresses low-light challenges in copper mine waste rock detection.
  • The integration of IAT and LEGM modules, along with MPDIoU loss, significantly enhances sorting accuracy and efficiency.
  • This advancement offers a cost-effective solution for improving environmental management in copper mining operations.