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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

Mohammad H Karimi1, Davud Asemani1

  • 1Laboratory of Signals and Electronic Systems, Electrical and Computer Engineering Faculty, K.N. Toosi University of Technology, Shariati Avenue, Tehran 1355-16315, Iran.

ISA Transactions
|February 8, 2014
PubMed
Summary
This summary is machine-generated.

Automated ceramic tile grading systems are crucial for quality control. This survey reviews image processing and pattern recognition algorithms for detecting various surface defects, aiding industry advancements.

Keywords:
Pattern recognitionSurface defectTiling

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Area of Science:

  • Materials Science
  • Computer Vision
  • Industrial Automation

Background:

  • Ceramic and tile industries require robust quality control.
  • Human-based grading is subjective and inefficient.
  • Automated defect detection enhances product quality and marketability.

Purpose of the Study:

  • To survey and categorize image processing and pattern recognition algorithms for ceramic tile surface defect detection.
  • To evaluate the effectiveness of different techniques for various defect types.
  • To provide a comprehensive overview for developing advanced automated grading systems.

Main Methods:

  • Categorization of defect detection techniques into statistical pattern recognition, feature vector extraction, and texture/image classification.
  • Review of pre-processing methods like wavelet transform, filtering, morphology, and contourlet transform.
  • Analysis of defect extraction algorithms including statistical methods, neural networks, and model-based approaches.

Main Results:

  • Different methods show varying effectiveness for specific defect subgroups (e.g., statistical methods for Spots, wavelet processing for Pinhole).
  • Pre-processing techniques are vital for enhancing defect detection accuracy.
  • A comprehensive comparison of algorithms using supervised and unsupervised evaluation parameters is presented.

Conclusions:

  • No single method is universally optimal for all defect types.
  • The choice of algorithm depends on the specific defects and desired precision.
  • Further research and development in automated defect detection are essential for the ceramic industry.