Open AccessResearch on Yarn Diameter and Unevenness Based on an Adaptive Median Filter Denoising Algorithm
Author(s) -
Xiao Wang,
Ru-Meng Hou,
Xiaoyan Gao,
Binjie Xin
Publication year - 2020
Publication title -
fibres and textiles in eastern europe
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 39
eISSN - 2300-7354
pISSN - 1230-3666
DOI - 10.5604/01.3001.0013.5856
Subject(s) - yarn , median filter , noise reduction , filter (signal processing) , noise (video) , image processing , artificial intelligence , segmentation , computer vision , algorithm , image (mathematics) , mathematics , computer science , materials science , composite material
In this paper an adaptive median filtering denoising algorithm is proposed to measure yarn diameter and its unevenness. Images of nine different yarn samples were captured using one set of a self-developed yarn image acquisition system. Image separation of the background and yarn sections was conducted using a combination of adaptive median filtering, adaptive threshold segmentation and morphological processing. The noise-free yarn image was used for diameter detection of the subsequent yarn image and the discrimination of the yarn unevenness. Experimental results show that the testing data of yarn unevenness detection based on the adaptive median filter denoising algorithm is very consistent with the data using the traditional method. It is proved that the yarn detection method proposed, based on an adaptive median filter denoising algorithm, is feasible. It can be used to calculate yarn diameter accurately and measure yarn unevenness efficiently, so as to determine the quality of yarn appearance objectively.