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Object detection algorithm based on improved YOLOv8 for drill pipe on coal mines.

Xiaojun Li1,2, Miao Li3, Mingyang Zhao3

  • 1School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo, 454003, China. lxj@hpu.edu.cn.

Scientific Reports
|February 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an improved object detection model to accurately count coal mine drill pipes, enhancing safety by improving gas extraction depth determination. The enhanced model boosts detection accuracy and recall rates in challenging underground conditions.

Keywords:
Deformable convolutionDrill pipe detectionDynamic headGas extractionYOLOv8n

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Area of Science:

  • * Mining Engineering
  • * Computer Vision
  • * Artificial Intelligence

Background:

  • * Coal mine gas extraction depth is crucial for disaster control, but current methods rely on manual drill pipe counting.
  • * Existing object detection algorithms struggle with poor performance in harsh coal mine environments due to factors like dust, mist, and variable lighting.
  • * Accurate and automated drill pipe counting is needed for real-time monitoring and effective gas extraction depth determination.

Purpose of the Study:

  • * To develop an improved object detection model for accurate and reliable drill pipe counting in coal mines.
  • * To address challenges posed by low illuminance, heavy dust, mist, and bright light interference.
  • * To enhance the real-time performance and detection accuracy of drill pipe counting algorithms.

Main Methods:

  • * Implemented an improved object detection model incorporating ACE dehazing for image quality enhancement.
  • * Integrated deformable convolution (DCNv2) and SimAM attention mechanism to improve feature extraction and detection confidence.
  • * Utilized a dynamic head and SIoU loss function to enhance scale, space, and channel feature extraction and address angle differences.
  • * Validated the model using a custom drill pipe dataset.

Main Results:

  • * The improved model significantly alleviated detection issues in scenes with heavy dust, mist, and uneven illumination.
  • * Achieved a 4.9% increase in recall rate and a 5.3% improvement in mean average precision (mAP).
  • * Maintained high real-time performance with a frames per second (FPS) of 117.

Conclusions:

  • * The enhanced object detection model provides a robust solution for automated drill pipe counting in challenging coal mine environments.
  • * The improvements in accuracy and recall contribute to more reliable gas extraction depth determination, enhancing mine safety.
  • * The model's high real-time performance supports practical applications in real-time tracking for coal mine gas disaster control.