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Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection.

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Summary

An automated system for painted heating device surfaces combines deflectometry and bright light imaging for defect detection. Deep learning models, particularly ResNet-50, accurately classify surfaces, improving industrial quality control.

Keywords:
ResNet-50automated quality controldefect detection and classificationilluminationtransfer learning

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Area of Science:

  • Industrial Automation
  • Computer Vision
  • Materials Science

Background:

  • Automated quality control is crucial for manufacturing efficiency.
  • Defect detection in painted surfaces presents challenges due to varying defect types and illumination conditions.
  • Current methods may lack robustness or require high computational resources.

Purpose of the Study:

  • To develop and evaluate an automated system for detecting and classifying defects on painted surfaces of heating devices.
  • To integrate deflectometry and bright light illumination for enhanced defect identification.
  • To assess the performance of deep learning models for defect classification and compare different algorithmic approaches.

Main Methods:

  • Image acquisition using combined deflectometry and bright light illumination.
  • Development and training of deep learning models (custom, ResNet-50, Inception V3) for surface defect classification (OK/NOK).
  • Fusion of dual-modal information at the decision level and implementation of an online network for data dispatching and visualization.

Main Results:

  • The dual-illumination approach expanded the range of detectable defects while maintaining low computational complexity through decision-level fusion.
  • Pre-trained deep learning networks (ResNet-50, Inception V3) outperformed the custom-built network in accuracy.
  • ResNet-50 achieved the highest accuracy in defect classification.
  • The system demonstrated consistent speed and accuracy, particularly when models were trained with data from both illumination modes.
  • Surface information was successfully transmitted to a server for graphical user interface visualization.

Conclusions:

  • The developed automated system effectively detects and classifies surface defects on painted heating devices.
  • Combining deflectometry and bright light illumination with deep learning, especially ResNet-50, offers a robust and efficient solution for industrial quality control.
  • Decision-level fusion of multi-modal information is key to maintaining accuracy and computational efficiency.