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Related Experiment Video

Updated: May 22, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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A lightweight algorithm for steel surface defect detection using improved YOLOv8.

Shuangbao Ma1,2, Xin Zhao2, Li Wan3

  • 1Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, 430073, China.

Scientific Reports
|March 16, 2025
PubMed
Summary
This summary is machine-generated.

A new lightweight algorithm improves steel surface defect detection by reducing model complexity and increasing accuracy. This enhanced YOLOv8 model offers a balance between performance and efficiency for edge computing applications.

Keywords:
Attention mechanismsDefect detectionGhostNetLightweightSIoUYOLOv8

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Materials Science

Background:

  • Steel surface defect detection faces challenges with low precision, high parameter counts, and complex models.
  • Existing methods struggle with efficiency in resource-constrained environments.

Purpose of the Study:

  • To develop a lightweight and accurate algorithm for steel surface defect detection.
  • To improve detection precision and reduce model complexity for edge deployment.

Main Methods:

  • An improved YOLOv8 algorithm incorporating GhostNet as the backbone.
  • Integration of the MultiPath Coordinate Attention (MPCA) mechanism for enhanced feature extraction.
  • Utilizing Simplified IoU (SIoU) loss function for faster and more accurate anchor regression.

Main Results:

  • Achieved a 37% reduction in computational load and a 32% decrease in parameter count.
  • Increased average detection accuracy (mAP) by 1.2%.
  • Demonstrated a balance between model lightweighting and detection accuracy.

Conclusions:

  • The proposed algorithm offers a viable solution for steel surface defect detection in resource-limited edge computing environments.
  • The enhancements provide a robust and efficient model for embedded systems and mobile devices.