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Research on an Improved YOLOv8 Detection Method for Surface Defects of Optical Components.

Bei Ma1, Jialong Zhao1, Shun Zhou1

  • 1School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710021, China.

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Summary

This study introduces BACG-YOLOv8, an enhanced object recognition algorithm for detecting surface defects on optical components. The improved model achieves higher accuracy and efficiency in identifying critical flaws, crucial for advanced applications.

Keywords:
attention mechanismdeep learningoptical componentssurface defects

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Area of Science:

  • Optics and Materials Science
  • Computer Vision and Artificial Intelligence

Background:

  • Optical components are vital in aerospace, microelectronics, and precision measurement.
  • Surface defects degrade system performance, requiring advanced detection methods.
  • Current defect detection methods face challenges in resolution, precision, and efficiency.

Purpose of the Study:

  • To develop a high-resolution, high-precision, and efficient optical surface defect detection algorithm.
  • To improve the performance of object recognition models for optical component defect analysis.
  • To address the limitations of existing methods in complex detection scenarios.

Main Methods:

  • An improved YOLOv8 object recognition algorithm was proposed.
  • The BRA attention mechanism was integrated into the YOLOv8 backbone for enhanced multi-scale feature processing.
  • The Context-GuideFPN module replaced the original feature fusion module for adaptive multi-scale feature integration.
  • The algorithm was trained and evaluated on a high-quality microscopic dark-field image dataset.

Main Results:

  • The enhanced BACG-YOLOv8 demonstrated excellent performance in optical component defect detection.
  • The optimized network accurately extracted defect details, including refined edge features.
  • The algorithm effectively suppressed noise interference, reducing detection errors.
  • Improved defect extraction accuracy was achieved compared to baseline models.

Conclusions:

  • The BACG-YOLOv8 algorithm offers a significant advancement in optical surface defect detection.
  • The integration of BRA attention and Context-GuideFPN enhances adaptability and accuracy in complex scenarios.
  • This approach provides a robust solution for ensuring the quality and performance of optical components.