• DocumentCode
    2077591
  • Title

    Novel approaches for detecting fabric fault using Artificial Neural Network with K-fold validation

  • Author

    Andalib, Ahmed Shayer ; Islam, Md Rafiqul ; Salekin, A. ; Abdulla-Al-Shami, Md

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Bangladesh Univ. of Eng. & Technol., Dhaka, Bangladesh
  • fYear
    2012
  • fDate
    22-24 Dec. 2012
  • Firstpage
    55
  • Lastpage
    60
  • Abstract
    In this paper we have proposed a novel method to detect the defects in woven fabric based on the abrupt changes in the intensity of fabric image due to the defects and have constructed a classification model to properly identify the defects. We have also improved an existing method based on histogram processing for the classifier. In classification model we have implemented Artificial Neural Network (ANN). Both of our newly proposed method and improved technique have outperformed the existing methods. We have implemented K-validation to estimate the performance of our classification model. Additionally we have analyzed the performance of our classification model for different experimental parameters. Finally we have presented a comparative analysis of these techniques.
  • Keywords
    fabrics; image classification; neural nets; object detection; production engineering computing; woven composites; ANN; K-fold validation; artificial neural network; classification model; fabric fault detection; fabric image; histogram processing; woven fabric; Adaptive Median Filter; Artificial Neural Network; K-Validation; Roberts Operator;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Technology (ICCIT), 2012 15th International Conference on
  • Conference_Location
    Chittagong
  • Print_ISBN
    978-1-4673-4833-1
  • Type

    conf

  • DOI
    10.1109/ICCITechn.2012.6509767
  • Filename
    6509767