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Intelligent Coal-Gangue Sorting Method Based on CCD and DE-XRT Image Fusion.

Ying Jia1,2,3,4, Baoshan Li5, Yongxing Du5

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This study introduces a novel intelligent coal-gangue sorting method by fusing industrial Charge Coupled Device (CCD) and Dual Energy X-ray Transmission (DE-XRT) images. The fused approach significantly enhances sorting accuracy and efficiency, achieving 100% recognition.

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Area of Science:

  • Materials Science
  • Computer Science
  • Engineering

Background:

  • Single-modal ore sorting faces challenges like missed detection, false positives, and low accuracy in complex environments.
  • Existing methods struggle with accuracy and efficiency in distinguishing coal from gangue.

Purpose of the Study:

  • To develop an intelligent coal-gangue sorting method that overcomes limitations of single-modal approaches.
  • To improve accuracy, reduce false detections, and enhance efficiency in complex sorting environments.

Main Methods:

  • Collected industrial Charge Coupled Device (CCD) and Dual Energy X-ray Transmission (DE-XRT) images of coal and gangue.
  • Applied adaptive weighted fusion for preprocessing X-ray images and constructed a dual-branch MobileNetV4 network.
  • Integrated the SimAM attention mechanism to boost feature representation and recognition accuracy.

Main Results:

  • The fused image approach significantly reduced computational complexity (FLOPs by 64.9%) and model parameters (by 61.7%).
  • Recognition accuracy improved by 7.6% (vs. CCD) and 28.3% (vs. XRT).
  • Achieved 100% recognition accuracy with no missed or false detections, demonstrating real-time sorting capability.

Conclusions:

  • The proposed fusion method effectively combines surface and internal feature information for superior coal-gangue sorting.
  • This intelligent sorting system offers a robust solution for complex environments, enhancing both performance and efficiency.
  • The lightweight network architecture ensures practical applicability for real-time industrial sorting.