A two-scale attention model for intelligent evaluation of yarn surface qualities with computer vision

Sheng Yan Li, Bingang Xu, Hong Fu, Xiaoming Tao, Zheru Chi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


Statistical features are extracted to characterize the yarn body and hairiness. In relative feature extraction, a two-scale attention model is proposed and developed, which can fully imitate human attention at different observation distances for the whole and detailed yarn information. Global and individual Probabilistic Neural Networks (PNNs) are then designed for yarn quality evaluation based on eight-grade and five-grade classifications. A database, covering eight yarn densities (Ne7~ Ne80) and different surface qualities, was constructed with 296 yarn board images for the evaluation. The accuracy for eight- and five-grade global PNNs are 92.23 and 93.58%, respectively, demonstrating a good classification performance of the proposed method.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalJournal of the Textile Institute
Publication statusAccepted/In press - 31 Aug 2017


  • classification
  • computational attention model
  • DPI determination
  • feature extraction
  • probabilistic neural network
  • Yarn evaluation

ASJC Scopus subject areas

  • Materials Science (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Polymers and Plastics
  • Industrial and Manufacturing Engineering

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