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Real-Time Belt Deviation Detection Method Based on Depth Edge Feature and Gradient Constraint.

Xinchao Xu1,2, Hanguang Zhao1, Xiaotian Fu1

  • 1School of Geomatics, Liaoning Technical University, Fuxin 123000, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time belt deviation detection method using ResNet18 and an attention mechanism. The novel approach significantly improves detection speed and accuracy, outperforming existing techniques for industrial applications.

Keywords:
deep learningdepth edge featuresgradient constraintsreal-time belt deviation detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Existing belt deviation detection methods suffer from poor recognition effects and low accuracy.
  • Real-time monitoring is crucial for safety and efficiency in various industrial transport systems.

Purpose of the Study:

  • To propose a novel, real-time belt deviation detection method with enhanced accuracy and speed.
  • To improve upon the limitations of current belt deviation detection techniques.

Main Methods:

  • Utilized ResNet18 with an attention mechanism for enhanced feature extraction of belt edges.
  • Employed a contextual classifier and an improved gradient equation for structural loss during model training.
  • Applied the least squares method for accurate belt edge line fitting and deviation threshold comparison.

Main Results:

  • The proposed method achieves a speed of 41 frames/sec, outperforming comparative methods.
  • Demonstrated significant accuracy improvements of 0.4% to 78.8% over four other detection techniques.
  • Achieved F1-score improvements ranging from 0.3% to 72%, meeting practical engineering application requirements.

Conclusions:

  • The developed real-time belt deviation detection method offers superior performance in speed and accuracy.
  • This technique is suitable for intelligent monitoring and control in coal mines, logistics, and transport industries.
  • The method addresses the limitations of existing approaches, paving the way for more reliable industrial monitoring.