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Related Concept Videos

Flat Belts: Problem Solving01:28

Flat Belts: Problem Solving

Flat belts are crucial in many industrial applications as they help transmit power from one pulley to another. The concept of forces and moments is used to determine the maximum moment on a pulley. For instance, consider a flat belt that wraps around two pulleys, A and B, with radii of 30 cm and 10 cm, respectively. The angle between the belt and the horizontal is 20 degrees at the pulleys. As pulley B rotates clockwise and drives pulley A, tension T2 is caused at one end of the belt, while...
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Frictional Forces on Flat Belts

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Related Experiment Videos

Conveyor belt foreign object detection method based on improved YOLOv11 and ESRGAN.

Qiang Li1, Ruocheng Zeng2, Guohua Wang3

  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.

Scientific Reports
|May 12, 2026
PubMed
Summary

This study introduces YOLOv11-CBFD, an improved object detection algorithm for conveyor belts. It enhances foreign object recognition in challenging low-light and low-resolution factory conditions, balancing accuracy and efficiency.

Keywords:
ESRGANEmbedded systemObject detectionYOLOv11

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Traditional conveyor belt object detection struggles with low-light and low-resolution images.
  • Existing methods lack robustness and adaptability in industrial settings.

Purpose of the Study:

  • To develop an improved object detection algorithm for conveyor belt foreign object detection.
  • To enhance recognition accuracy and computational efficiency in challenging industrial environments.

Main Methods:

  • Utilized the YOLOv11 object detection framework with architectural improvements.
  • Integrated Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) for image enhancement.
  • Developed a custom dataset for factory conveyor belt foreign object detection.

Main Results:

  • The proposed YOLOv11-CBFD achieved 86.1% accuracy, 86.7% recall, and 89.1% F1-score.
  • Reduced parameter count by 16.2% compared to YOLOv11n, with a 12.4% increase in FPS on Jetson Orin NX.
  • Demonstrated 48-hour continuous operation with <33 ms/frame latency in field tests.

Conclusions:

  • YOLOv11-CBFD effectively addresses foreign object detection challenges on conveyor belts.
  • The algorithm achieves a strong balance between detection performance and computational efficiency.
  • Validated practical effectiveness for real-time industrial inspection applications.