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Research on Yarn Diameter and Unevenness Based on an Adaptive Median Filter Denoising Algorithm

Research and development

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Nr DOI: 10.5604/01.3001.0013.5856

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Abstract:

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.

Tags:

adaptive median filtering, denoising algorithm, yarn diameter, unevenness, image processing.

Citation:

Wang X, Hou R-M, Gao X-Y, Xin B-J. Research on Yarn Diameter and Unevenness Based on an Adaptive Median Filter Denoising Algorithm. FIBRES & TEXTILES in Eastern Europe  2020; 28, 1(139): 36-41. DOI: 10.5604/01.3001.0013.5856

Published in issue no 1 (139) / 2020, pages 36–41.

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