Measurement of yarn apparent evenness based on modified Canny edge detection

Abstract

To evaluate accurately yarn apparent evenness, a simple yet effective method based on modified Canny edge detection was presented. Firstly, the originally captured yarn image is processed by Gaussian filtering, and the weighted least square method is used to calculate the horizontal and vertical directions of the image gradient for obtaining gradient images. Then the non-maximum suppression is applied to process the obtained gradient images and the double threshold algorithm is used to get the yarn edge curve. Finally, the average yarn diameter and the yarn coefficient variation (CV) of yarn were calculated via the number of pixels at the upper and lower edge points of the yarn evenness. To verify the validity and accuracy of the method, yarns with different linear densities were tested. The experimental results demonstrate that the proposed method can accurately detect the unevenness of the yarn apparent diameter.

Keywords:

• Yarn evenness
• Canny operator
• yarn quality evaluation
• coefficient variation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Natural Science Foundation of China under Grant 61902302, in part by Shaanxi Provincial Key R&D Program Project under Grant 2021GY-261, the Preferential Funding for Post-Doctoral Research Program in Zhe Jiang Province under Grant ZJ2022154, the Science and Technology Foundation of Xi’an for Program of University Science and Technology Scholar Serving Enterprise under Grant 22GXFW0033, the Doctoral Scientific Research Foundation of Xi’an Polytechnic University under Grant BS202002, Shaanxi Provincial Department of Education Youth Innovation Team Project under Grant 21JP050, The Youth Innovation Team of Shaanxi Universities, and Innovation Capability Support Program of Shaanxi under Grant 2021TD-29.