Integrated digital system for yarn surface quality evaluation using computer vision and artificial intelligence

Sheng Yan Li, Jie Feng, Bingang Xu, Xiaoming Tao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The evaluation method of yarn surface quality currently in use is mainly based on manual inspection. In order to resolve the inherent limitations of the human visual inspection, an intelligent evaluation system has been developed for the objective and automatic evaluation of yarn surface quality with computer vision and artificial intelligence. In this system, all yarn surface features are fully digitalized and quantitatively processed to ensure an objective evaluation of yarn surface appearance. This digital system integrates and controls the whole progress of yarn surface analysis, including the image acquisition, digital feature extraction, characteristic parameter computation and yarn quality classification, in one computer program with an interactive and friendly user interface. Besides yarn quality classification, multiple yarn surface characteristics, such as yarn diameter irregularities, yarn fault areas, foreign matters and fuzziness, can also be quantitatively obtained and visibly displayed.
Original languageEnglish
Title of host publicationProceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
Pages472-476
Number of pages5
Volume1
Publication statusPublished - 1 Dec 2012
Event2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012 - Las Vegas, NV, United States
Duration: 16 Jul 201219 Jul 2012

Conference

Conference2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012
CountryUnited States
CityLas Vegas, NV
Period16/07/1219/07/12

Keywords

  • Artificial neural network
  • Image processing
  • Intelligent system
  • Yarn evaluation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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