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Research on Coal and Gangue Recognition Based on the Improved YOLOv7-Tiny Target Detection Algorithm.

Yiping Sui1, Lei Zhang1,2, Zhipeng Sun1

  • 1College of Coal Engineering, Shanxi Datong University, Datong 037003, China.

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This study introduces an improved YOLOv7-tiny model for accurate coal and gangue recognition in challenging mine environments. The enhanced model achieves high precision and speed, crucial for intelligent mine construction.

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YOLOv7-tiny modelablation experimentartificial intelligence algorithmmodel performance evaluation indexrecognition of coal and gangue

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Mining Engineering

Background:

  • Intelligent mine construction relies on accurate coal and gangue recognition.
  • Existing models struggle with low accuracy and small targets in dusty, low-light mine conditions.

Purpose of the Study:

  • To develop a robust coal and gangue recognition model for challenging mining environments.
  • To improve recognition accuracy and speed for small targets.

Main Methods:

  • An improved YOLOv7-tiny algorithm incorporating coordinate attention and contextual transformer modules.
  • Weighted cascading of feature pyramid network modules for enhanced feature extraction.
  • Validation through field tests in coal mine environments.

Main Results:

  • The improved YOLOv7-tiny model achieved a 97.54% mean average precision.
  • The model demonstrated a recognition speed of 24.73 frames per second.
  • Outperformed established models like Faster-RCNN, YOLOv3, YOLOv4, and YOLOv5s in recognition rate and speed.

Conclusions:

  • The proposed enhanced YOLOv7-tiny model offers a significant advancement in coal and gangue recognition technology.
  • This provides an effective solution for accurate identification in intelligent mine construction.
  • The model's performance is validated in real-world coal mine conditions.