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ILCS: An Improved Lightweight Convolution Structure and Mixed Interactive Attention for Steel Surface Defect

Yangjun Pei1, Mingyang Hou1, Qi Han1

  • 1School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.

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Summary
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This study introduces a lightweight deep learning model for steel surface defect classification. The model achieves high accuracy with reduced parameters and computation, making it suitable for industrial quality control on edge devices.

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Area of Science:

  • Materials Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Steel surface defect classification is crucial for industrial quality control.
  • Existing deep learning models face challenges with limited computing resources in production environments.
  • Developing lightweight models for rapid and accurate classification is essential.

Purpose of the Study:

  • To propose an improved lightweight convolution structure (LCS) for efficient steel surface defect classification.
  • To enhance classification accuracy by integrating attention mechanisms.
  • To reduce model parameters and computational load for edge deployment.

Main Methods:

  • Developed a novel lightweight convolution structure (LCS) using separable convolutions, depthwise convolutions, and point-wise convolutions.
  • Integrated spatial and channel attention mechanisms to mitigate accuracy loss from lightweight convolutions.
  • Introduced a mixed interactive attention module (MIAM) to further boost feature extraction.

Main Results:

  • The proposed method significantly reduces the number of model parameters and computational complexity.
  • Achieved higher recognition accuracy compared to traditional deep learning models.
  • Demonstrated the effectiveness of the LCS and MIAM for lightweight steel surface defect classification.

Conclusions:

  • The developed lightweight model successfully balances efficiency and accuracy for steel surface defect classification.
  • The approach is well-suited for deployment on edge devices with limited computational power in industrial settings.
  • This work contributes to advancing automated quality control in steel manufacturing.