Abstract
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 language | English |
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Pages (from-to) | 798–812 |
Journal | Journal of the Textile Institute |
Volume | 109 |
Issue number | 6 |
DOIs | |
Publication status | Published - Aug 2018 |
Keywords
- classification
- computational attention model
- DPI determination
- feature extraction
- probabilistic neural network
- Yarn evaluation
ASJC Scopus subject areas
- Materials Science (miscellaneous)
- General Agricultural and Biological Sciences
- Polymers and Plastics
- Industrial and Manufacturing Engineering