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Computer Vision-Based Techniques for Conveyor Belt Condition Monitoring: A Systematic Review.

Pablo Rios-Colque1, Victor Rios-Colque1, Luis Rios-Colque2

  • 1Ingeniería Mecánica-Electromecánica-Mecatrónica, Facultad Nacional de Ingeniería, Universidad Técnica de Oruro, Oruro, Bolivia.

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Computer vision techniques are advancing conveyor belt monitoring in mining. Deep learning models show improved damage, deviation, and foreign object detection, enhancing operational efficiency and safety.

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Area of Science:

  • Engineering
  • Computer Science
  • Materials Science

Background:

  • Conveyor belts are vital for mining efficiency, requiring continuous monitoring.
  • Traditional monitoring methods face limitations in speed and accuracy.
  • Computer vision offers a promising automated solution for condition assessment.

Purpose of the Study:

  • To systematically review computer vision techniques for conveyor belt condition monitoring.
  • To analyze trends, research lines, and methodologies in the field.
  • To identify current challenges and future opportunities for industrial adoption.

Main Methods:

  • Systematic literature review following PRISMA guidelines.
  • Searches conducted in Scopus and Web of Science databases.
  • Quantitative bibliometric analysis and qualitative thematic categorization of 80 selected studies.

Main Results:

  • Significant research output increase post-2020, with identified geographic trends.
  • Key research areas include damage, deviation, and foreign object detection.
  • Shift from traditional image processing to deep learning models (e.g., YOLO, CenterNet) showing enhanced performance.

Conclusions:

  • Deep learning models offer superior precision, speed, and stability for conveyor belt monitoring.
  • Challenges include dataset limitations, varied evaluation protocols, and operational variability.
  • Future directions involve multimodal data, adaptive models, and lightweight solutions for industrial integration.