A method to evaluate the diameter of carded cotton yarn using image processing and artificial neural networks

Research Field:Mechanical Engineering/Textile/Image Processing

Research Content:

Yarn diameter is one of the most important properties that influence several properties of end products like cover factor, appearance and handle properties of the fabrics. As thousands of ends or loops are presented side-by-side in the woven or in the knitted fabrics, a slight change in yarn diameter can result in a substantial change in the overall cover factor of fabric. The factors, which affect the yarn diameter, also have an effect on the yarn density and fibre compactness.

A new method was proposed to determine the diameter of cotton yarn using digital image processing and artificial neural networks. After processing scanned yarn image, the inputs of ANN were represented by yarn count and the white pixels number of edge detection of processed image by Canny’s, Sobel’s and Prewiit’s algorithms. The output of ANN was the diameter of cotton yarn.

Innovations & Advantages:

More realistic results, saving money and time, knowing yarn diameter without microscope.


Textile manufacturers such as spinning, weaving and knitting mills … etc.