Objective evaluation on yarn hairiness detection based on multi-view imaging and processing method

Abstract

In this paper, a multi-view yarn image acquisition device was proposed to collect yarn images from many different viewing angles instead of a single viewing angle, for the purpose of obtaining the expected accurate measurement. One set of the proposed image processing algorithms, quite qualified for processing the multi-view yarn image sequences, was employed to obtain the shape of the yarn hairiness viewed from different angles. Both lengths and numbers of yarn hairiness from different viewing angles could be identified, and besides, the average value of these hairiness parameters could be calculated to determine the quality of yarn hairiness. Our experimental results show that the multi-view imaging and processing method could be used to avoid the maximum or minimum value of the detection results, with more comprehensive yarn hairiness parameter information. In addition, as the guidance for the subsequent processing on yarn products, the processing results obtained from multi-view imaging and processing algorithm are characterized by reproducible, convenient for further study of yarn hairiness. Combined with the existing image processing algorithms, the multi-view image acquisition device put forward in this paper could be adopted to form a complete yarn hairiness detection system, providing a favorable theoretical support for the future development of digital yarn quality evaluation system.