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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
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.
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.
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