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Defect Detection in Steel Using a Hybrid Attention Network.

Mudan Zhou1, Wentao Lu2, Jingbo Xia1

  • 1School of Information Science & Technology, Xiamen University Tan Kah Kee College, Zhangzhou 363105, China.

Sensors (Basel, Switzerland)
|August 12, 2023
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Summary

This study introduces a hybrid attention network for improved steel surface defect detection. The novel method enhances feature learning and multi-scale information extraction, significantly boosting detection accuracy and outperforming existing object detection algorithms.

Keywords:
ASFFCIOUEfficientNetSSDYOLOV3YOLOV5deep learningdefect detectionmAPsteel surfaces

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Area of Science:

  • Materials Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Steel surface defect detection is crucial for quality control.
  • Deep learning methods show promise but require accuracy improvements.
  • Existing algorithms face challenges in precise defect identification and localization.

Purpose of the Study:

  • To propose a hybrid attention network for enhanced steel surface defect detection.
  • To improve the accuracy and feature learning capabilities of defect detection models.
  • To address the limitations of current deep learning algorithms in this field.

Main Methods:

  • A hybrid attention network incorporating CBAM and ASFF modules was developed.
  • CBAM (Convolutional Block Attention Module) enhances feature learning.
  • ASFF (Adaptive Spatial Feature Fusion) extracts multi-scale defect information.
  • CIOU (Complete Intersection over Union) loss optimization was employed.

Main Results:

  • The proposed method achieved superior performance on the NEU-DET dataset.
  • An 8.34% improvement in mean Average Precision (mAP) was observed.
  • Significant mAP gains were recorded against SSD, EfficientNet, YOLOV3, and YOLOV5.

Conclusions:

  • The hybrid attention network demonstrates superior accuracy in steel surface defect detection.
  • The integration of CBAM, ASFF, and CIOU effectively enhances detection capabilities.
  • This approach offers a promising advancement over existing object detection algorithms for industrial applications.