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Research on improved gangue target detection algorithm based on Yolov8s.

Zhibo Fu1, Xinpeng Yuan1, Zhengkun Xie1

  • 1School of Coal Engineering, Shanxi Datong University, Datong, China.

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Summary

This study introduces an enhanced deep learning algorithm for coal gangue detection, significantly improving speed and accuracy while reducing model size and computational cost for efficient sorting.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning models for coal gangue target detection face challenges with slow speeds, high parameter counts, and significant computational costs.
  • Optimizing these models is crucial for developing efficient and real-time gangue sorting systems.

Purpose of the Study:

  • To develop an improved deep learning algorithm based on Yolov8s for coal gangue target detection.
  • To enhance detection speed, reduce model complexity, and increase accuracy compared to existing methods.

Main Methods:

  • Utilized Fasternet as a lightweight backbone to increase object detection speed and reduce model complexity.
  • Replaced Slimneck with C2F in the HEAD module and the Detect layer with Detect-DyHead to improve accuracy.
  • Incorporated DIoU loss function and BAM block attention mechanism to enhance feature focus and detection performance.

Main Results:

  • Achieved a 28% reduction in model storage size.
  • Reduced the number of parameters by 28.8% and computational effort by 34.8%.
  • Improved detection accuracy by 2.5% compared to the original Yolov8s model.

Conclusions:

  • The Yolov8s-change model offers a fast, real-time, and efficient solution for coal gangue detection and sorting.
  • This advancement provides strong support for the intelligent sorting of coal gangue, addressing key limitations of previous deep learning approaches.