Yarn Quality detection for Textile Industries using Image Processing
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Abstract
In textile industry, estimating Yarn’s Quality becomes difficult task. In common cases; the task is completed manually. A Microscopic investigation for yarn quality estimation necessitates significant amount of physical efforts as well as timing, as well as compromise on quality judgment uniformity. It’s nevertheless, a classic challenge during Yarn-based studies, whereas accurate yarn’s quality manufacture is determined using mathematical Yarn attributes such as length, and Diameter among others. In Previous researches, it is observed that Yarn Quality is calculated only depends on Yarn’s Length and Diameter. However, these criteria alone do not allow for many mixing permutations and combinations in order to make various quality variant Yarns. We suggested in this paper to develop more Yarn characteristics from yarn (cotton) with much more precision, like Yarn’s regularity throughout the yarn thickness, hairiness, small colour change, or contaminants.