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Related Experiment Videos

DAER-YOLO: Defect-Aware and Edge-Reconstruction Enhanced YOLO for Surface Defect Detection of Varistors.

Wu Xie1,2, Shushuo Yao1,2, Tao Zhang3

  • 1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.

Journal of Imaging
|May 26, 2026
PubMed
Summary

Detecting surface defects in varistors is crucial for power electronics. A new DAER-YOLO model improves detection accuracy for small defects and edge damage, ensuring system stability and enabling real-time industrial applications.

Keywords:
YOLOattention mechanismdeep learningdefect detecting

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Materials Science
  • Computer Vision

Background:

  • Varistors are essential for overvoltage protection in power electronics, safeguarding equipment.
  • Surface defects in varistors can degrade performance and lead to system failures.
  • High-precision defect detection is vital for quality control and reliability.

Purpose of the Study:

  • To develop an efficient and accurate method for detecting surface defects in varistors.
  • To improve the detection of small and morphologically complex defects.
  • To enhance the model's capability for edge damage reconstruction.

Main Methods:

  • Proposed a lightweight model: Defect-Aware and Edge-Reconstruction Enhanced YOLO (DAER-YOLO).
  • Introduced a C3k2-based defect-aware enhancement module (C3k2-iEMA) for detailed feature extraction.
  • Developed an Efficient Up-Convolution Block (EUCB) for improved feature fusion and edge reconstruction.

Main Results:

  • DAER-YOLO demonstrated superior performance over the YOLOv11n baseline on a custom varistor dataset.
  • Achieved a 1.6% increase in mAP@50 and a 2.3% increase in mAP@50:95.
  • The model effectively improved detection accuracy for varistor surface defects.

Conclusions:

  • DAER-YOLO offers an effective solution for high-precision varistor surface defect detection.
  • The model shows significant potential for real-time industrial inspection applications.
  • Improved defect detection enhances the reliability and stability of power electronic systems.