Intelligent Real-Time Fabric Defect Detection

  • Hugo Peres Castilho
  • Paulo Jorge Sequeira Gonçalves
  • João Rogério Caldas Pinto
  • António Limas Serafim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4633)


This paper presents real-time fabric defect detection based in intelligent techniques. Neural networks (NN), fuzzy modeling (FM) based on product-space fuzzy clustering and adaptive network based fuzzy inference system (ANFIS) were used to obtain a clearly classification for defect detection. Their implementation requires thresholding its output, and based in previous studies a confusion matrix based optimization is used to obtain the threshold. Experimental results for real fabric defect detection were obtained from the experimental apparatus presented in the paper, that showed the usefulness of the three intelligent techniques, although the NN has a faster performance. Online implementation of the algorithms showed they can be easily implemented with commonly available resources and may be adapted to industrial applications without great effort.


Defect Detection Fuzzy Modeling Fuzzy Inference System Fuzzy Cluster Confusion Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hugo Peres Castilho
    • 3
  • Paulo Jorge Sequeira Gonçalves
    • 1
    • 2
  • João Rogério Caldas Pinto
    • 1
  • António Limas Serafim
    • 3
  1. 1.IDMEC/IST, Technical University of Lisbon (TU Lisbon), Av. Rovisco Pais, 1049-001 LisboaPortugal
  2. 2.EST, Polytechnical Institute of Castelo Branco, Av. do Empresário, 6000-767 Castelo BrancoPortugal
  3. 3.INETI, Instituto Nacional de Engenharia Tecnologia e Inovação, Estrada do Paço do Lumiar, 22, 1649-038 LisboaPortugal

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