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Research on lightweight algorithm for gangue detection based on improved Yolov5.

Xinpeng Yuan1, Zhibo Fu2, Bowen Zhang3

  • 1School of Coal Engineering, Shanxi Datong University, Datong, 037000, China. XpnYuan@163.com.

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Summary

This study introduces an improved deep learning algorithm for coal gangue detection, significantly enhancing speed and reducing model complexity. The optimized method offers a practical reference for intelligent coal gangue classification systems.

Keywords:
Attention mechanismCoal gangue recognitionEfficientVITLoss functionYolov5s

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning methods for gangue detection face challenges with slow speeds, numerous parameters, and high computational demands.
  • Existing models require optimization for efficient real-time applications in intelligent coal processing.

Purpose of the Study:

  • To develop an optimized deep learning algorithm for faster and more efficient gangue target detection.
  • To reduce model complexity, parameter count, and computational cost while maintaining or improving detection performance.

Main Methods:

  • The proposed algorithm utilizes Yolov5s as a base, incorporating the EfficientViT lightweight network as the backbone.
  • Modifications include replacing the C3 module with C3_Faster, removing the 20x20 feature map branch, and implementing the MpIoU loss function.
  • An SE attention mechanism was introduced to enhance focus on critical features.

Main Results:

  • The improved model achieved a 77.8% reduction in size and computational cost.
  • The number of parameters was reduced by 78.3%, and detection speed increased by 30.6% (frames per second).
  • The MpIoU loss function and SE attention mechanism contributed to improved detection accuracy.

Conclusions:

  • The enhanced Yolov5s algorithm offers a significant improvement in efficiency for coal gangue detection.
  • The optimized model provides a viable solution for intelligent coal gangue classification, addressing previous limitations.