Fabric yarn detection based on improved fast R-CNN model

Fabric yarn detection based on improved fast R-CNN model

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Publicado en 3C TIC – Volume 12 Issue 1 (Ed. 42)


Haiyan Xu*



With the rapid development of modern computer technology, and gradually combined with the textile industry, the application of modern computer technology in the field of textile is increasingly extensive, which makes textile production gradually move towards the road of automation development. This paper proposes an automatic detection method of simple weave fabric density based on computer image vision. Computer vision and digital image processing technology are used to analyze and identify the simple weave fabric's warp and weft yarn information and calculate the fabric density. To avoid the phenomenon of warp and weft yarn skew, a method of fabric skew correction based on the Radon transform is proposed. The optimal decomposition order of these four fabrics is k = 2, k = 5, and k = 3. The decomposition series is k. It is found that the relative error of both warp and weft density is about 1.00%. Most of the data obtained by the method of correlation coefficient curve to determine the optimal decomposition series are consistent with the results of the energy curve method. The relative error of the density test results of No. 3 fabric, No. 6 fabric, and No. 7 fabric is higher than 10%, and the relative error of No. 3 fabric is the highest, reaching 66%. This shows serious errors in these three fabrics' warp and weft density. To solve the problems of simple weave fabric density detection, the corresponding algorithm is used to solve the problems. Finally, good results are obtained, which verifies the feasibility of this method. It is significant to realize the automatic measurement of fabric density in textile factories.


Palabras clave


Fabric yarn; Fast R-CNN algorithm; Visual inspection; Image processing; Wavelet decomposition.

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