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A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection.

Yizhe Li1, Yidong Xie1, Hu He1

  • 1State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

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This study introduces an optimized Faster R-CNN deep learning model for efficient aluminum surface defect detection. The advanced method achieves high accuracy, meeting industrial demands for quality control in aluminum manufacturing.

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Area of Science:

  • Materials Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Aluminum's widespread use in critical industries like aerospace and automotive necessitates robust quality control.
  • Existing surface defect detection methods lack the efficiency and accuracy required for modern industrial applications.
  • Surface defects significantly compromise the quality and safety of aluminum products.

Purpose of the Study:

  • To develop an innovative and highly accurate aluminum surface defect detection system.
  • To improve upon traditional defect detection methods by leveraging deep learning.
  • To meet the stringent efficiency and accuracy standards of industrial aluminum production.

Main Methods:

  • An optimized two-stage Faster R-CNN deep learning network was developed for defect detection.
  • A 2D camera with optimized lighting and focus captured high-resolution images for real-time analysis.
  • The network incorporated a multi-scale feature pyramid, an optimized Convolutional Block Attention Module (CBAM), and a lightweight Ghost model.
  • The genetic K-means algorithm was used for optimizing prior region selection.

Main Results:

  • The optimized network achieved a mean Average Precision (mAP) of 94.25% on a dataset of 3200 images.
  • Individual Average Precision (AP) values for specific defects exceeded 80%, surpassing industrial standards.
  • The Ghost model reduced network complexity by 14.3% while maintaining superior performance in accuracy, speed, and stability.
  • The system demonstrated enhanced defect recognition by integrating semantic and location information.

Conclusions:

  • The proposed deep learning-based method offers a significant advancement in aluminum surface defect detection.
  • The optimized Faster R-CNN network, incorporating CBAM and the Ghost model, provides high accuracy and efficiency.
  • This approach meets and exceeds current industrial requirements for reliable aluminum quality control.