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Steel surface defect detection method based on improved YOLOv9.

Cong Chen1, Hoileong Lee2, Ming Chen3

  • 1School of Marine Information Engineering, Hainan Tropical Ocean University, Sanya, 572022, China.

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|July 11, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances steel surface defect detection using an improved YOLOv9 algorithm. The novel approach significantly boosts accuracy and efficiency in identifying small defects, crucial for intelligent manufacturing quality control.

Keywords:
BiFPN moduleC3 moduleDSConv moduleDySample upsampling operatorSteel surface defect detectionYOLOv9

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Area of Science:

  • Materials Science and Engineering
  • Computer Vision and Artificial Intelligence
  • Industrial Automation

Background:

  • Steel surface defect detection is vital for quality control in industrial automation.
  • Diverse defect types and sizes, especially small ones, present significant detection challenges, leading to high error rates.
  • Existing methods struggle with subtle, small defects, impacting production efficiency and product quality.

Purpose of the Study:

  • To develop an improved steel surface defect detection algorithm based on YOLOv9.
  • To enhance the detection accuracy and efficiency of small-sized defects.
  • To reduce computational complexity and improve multi-scale target detection capabilities.

Main Methods:

  • Implemented Depthwise Separable Convolution (DSConv) to reduce model complexity.
  • Integrated the C3 module for effective multi-level feature fusion and multi-scale target detection.
  • Incorporated a bidirectional feature pyramid network (BiFPN) and DySample upsampling for improved small target feature extraction and localization.

Main Results:

  • Achieved a mean average precision (mAP) of 78.2%, an increase of 1.8% over the baseline.
  • Reached an accuracy of 82.5%, a 7.4% improvement compared to the baseline model.
  • Reduced the number of model parameters by 8.9% while enhancing performance.

Conclusions:

  • The improved YOLOv9 algorithm effectively addresses challenges in steel surface defect detection, particularly for small targets.
  • The proposed enhancements lead to significant improvements in detection accuracy, localization, and computational efficiency.
  • This research offers practical value for advancing quality control in intelligent manufacturing and steel production.