Abstract
Yarn hairiness is one of the key parameters influencing fabric quality. In this paper image processing and analysis algorithms developed for an automatic determination of yarn hairiness are presented. The main steps of the proposed algorithms are as follows: image preprocessing, yarn core extraction using graph cut method, yarn segmentation using high pass filtering based method and fibres extraction. The developed image analysis algorithms quantify yarn hairiness by means of the two proposed measures such as hair area index and hair length index, which are compared to the USTER hairiness index—the popular hairiness measure, used nowadays in textile science, laboratories and industry. The detailed description of the proposed approach is given. The developed method is verified experimentally for two distinctly different yarns, produced by the use of different spinning methods, different fibres types and characterized by totally different hairiness. The proposed algorithms are compared with computer methods previously used for yarn properties assessment. Statistical parameters of the hair length index (mean absolute deviation, standard deviation and coefficient of variation) are calculated. Finally, the obtained results are analyzed and discussed. The proposed approach of yarn hairiness measurement is universal and the presented algorithms can be successfully applied in different vision systems for yarn quantitative analysis.
Highlights
Dynamic development of machine vision techniques broadens the range of their applications
In modern computer vision systems image processing and analysis algorithms are used for an automatic measurement of important yarn quality parameters, such as hairiness [12,15,20,22,39,40], diameter [13,38], twist [17,41], thickness [17,49], faults [14], density and bulkiness [15], surface defects [29], etc
Modern high quality electron microscopes are used for yarn hairiness measurements [12,13]
Summary
Dynamic development of machine vision techniques broadens the range of their applications. For more than 30 years computer vision techniques have been used in textile science for yarn quality inspection [3,27,49]. Uneven effects that influence the appearance of a fabric and decrease its commercial value can appear at each phase of the production cycle [1]. Most commonly they are caused by defects of yarn from which the fabric is woven. Image analysis techniques are used for an automatic thread [35] and warp [16] quality analysis, and for estimating the dimensions of spliced connections of yarn-ends [19] and repetition of yarn structure [35], which influence fabric quality
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