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Fabric Defect Detection Using a Hybrid and Complementary Fractal Feature Vector and FCM-based Novelty Detector

Research and development

Authors:

  • Zhou Jian
    Key Lab of Textile Science & Technology, Ministry of Edycation, Dong Hua University, Shanghai, P. R. China
  • Zhou Jian
    Key Laboratory of Eco-textiles, Ministry of Education, Jiangnan University, WuXi, P. R. China
  • Wang Jun
    Key Lab of Textile Science & Technology, Ministry of Edycation, Dong Hua University, Shanghai, P. R. China
  • Bu Honggang
    North Valley Aircraft, Valley City, ND, USA

Nr DOI: 10.5604/01.3001.0010.5370

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Abstract:

Automated detect detection in woven fabrics for quality control is still a challenging novelty detection problem. This work presents five novel fractal features based on the box-counting dimension to address the novelty detection of fabric defect. Making use of the formation of woven fabric, the fractal features are extracted in a one-dimension series obtained by projecting a fabric image along the warp and weft directions, where their complementarity in discriminating defects is taken into account. Furthermore a new novelty detector based on fuzzy c-means (FCM) is devised to deal with one-class classification of the features extracted. Finally, by jointly applying the features proposed and the FCM based novelty detector, we evaluate the method proposed for eight datasets with different defects and textures, where satisfying results are achieved with a low overall missing detection rate.

Tags:

defect detection, box-counting dimension, fuzzy c-means, novelty detection.

Citation:

Zhou J, Wang J, Bu H. Fabric Defect Detection Using a Hybrid and Complementary Fractal Feature Vector and FCM-based Novelty Detector. FIBRES & TEXTILES in Eastern Europe 2017; 25, 6(126): 46-52. DOI: 10.5604/01.3001.0010.5370

Published in issue no 6 (126) / 2017, pages 46–52.

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