Abstract
In this research a computer vision system with artificial neural network is used for the identification and defect detection on several samples of the available textile's woven fabrics. Several textural features that are extracted by the neighboring grey level dependence matrix, and the grey level run length matrix is used as input data for the network trained by the backpropagation method. Results of the experiment indicate that the system can identify three product-types: plain, twill, and sateen weaves, and it can detect several defects, including pick-broken, pick-inhomogeneity, reed-mark, and dirt, with more than 80% correct.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
