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Related Experiment Video

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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Surface defect detection of steel based on improved YOLOv5 algorithm.

Yiwen Jiang1

  • 1School of Intelligent Equipment, Changzhou College of Information Technology, Changzhou 213164, China.

Mathematical Biosciences and Engineering : MBE
|December 5, 2023
PubMed
Summary

This study enhances the YOLOv5 steel surface defect detection model for better accuracy and efficiency. The improved algorithm achieves a higher mean average precision (mAP) while maintaining a fast processing speed.

Keywords:
EIOUSE-NetYOLOv5carafe upsamplingdefect detection

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Area of Science:

  • Materials Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Steel surface defect detection is crucial for quality control.
  • Existing methods face challenges in balancing detection accuracy and processing efficiency.
  • The YOLOv5 model offers a foundation but requires optimization for specific industrial applications.

Purpose of the Study:

  • To enhance the YOLOv5 model for improved steel surface defect detection.
  • To achieve a superior balance between accuracy and efficiency in defect identification.
  • To introduce novel algorithmic improvements for real-time industrial monitoring.

Main Methods:

  • Optimized anchor box localization using K-means++ algorithm.
  • Transitioned loss function from Generalized Intersection over Union (GIOU) to Efficient Intersection over Union (EIOU).
  • Implemented Carafe upsampling and integrated Squeeze and Excitation Networks (SE-Net) module.

Main Results:

  • Achieved a mean average precision (mAP) of 83.3%, a seven percentage point increase over the original YOLOv5.
  • Significantly reduced model size compared to other advanced algorithms.
  • Maintained a processing speed of 47 frames per second.

Conclusions:

  • The proposed enhancements effectively improve both the accuracy and efficiency of steel surface defect detection.
  • The optimized YOLOv5 model demonstrates strong performance for industrial applications.
  • This approach offers a viable solution for real-time, high-accuracy defect identification in steel manufacturing.