Liang, J.; Zhou, J.; Hu, X.; Luo, H.; Cao, G.; Liu, L.; Xiao, K. Digital Grading the Color Fastness to Rubbing of Fabrics Based on Spectral Reconstruction and BP Neural Network. Journal of Imaging 2023, 9, 251, doi:10.3390/jimaging9110251.
Liang, J.; Zhou, J.; Hu, X.; Luo, H.; Cao, G.; Liu, L.; Xiao, K. Digital Grading the Color Fastness to Rubbing of Fabrics Based on Spectral Reconstruction and BP Neural Network. Journal of Imaging 2023, 9, 251, doi:10.3390/jimaging9110251.
Liang, J.; Zhou, J.; Hu, X.; Luo, H.; Cao, G.; Liu, L.; Xiao, K. Digital Grading the Color Fastness to Rubbing of Fabrics Based on Spectral Reconstruction and BP Neural Network. Journal of Imaging 2023, 9, 251, doi:10.3390/jimaging9110251.
Liang, J.; Zhou, J.; Hu, X.; Luo, H.; Cao, G.; Liu, L.; Xiao, K. Digital Grading the Color Fastness to Rubbing of Fabrics Based on Spectral Reconstruction and BP Neural Network. Journal of Imaging 2023, 9, 251, doi:10.3390/jimaging9110251.
Abstract
To digital grade the color fastness of fabrics after rubbing, an automatic grading method based on spectral reconstruction technology and BP neural network was proposed. Firstly, the modeling samples are prepared by rubbing the fabrics according to the ISO standard of 105-X12. Then, to comply with visual rating standards for color fastness, the modeling samples are professionally graded to obtain the visual rating result. After that, a digital camera is used to capture digital images of the modeling samples inside a closed and uniform lighting box, and the color data values of the modeling samples are obtained through spectral reconstruction technology. Finally, the color fastness prediction model for rubbing was constructed using the modeling samples data and BP neural network. The color fastness level of the testing samples was predicted using the prediction model, and the prediction results were compared with the existing color difference conversion method and curve fitting method. Experimental show that the prediction model of fabric color fastness can be better constructed using the BP neural network, where the root-mean-square error of the prediction for the training sample is 0.30, and the root-mean-square error of the prediction for the test sample is 0.25. The overall performance of the method is slightly better than the color difference conversion method, and it is significantly outperforming the curve fitting method. It can be seen that the digital rating method of fabric color fastness to rubbing based on spectral reconstruction and BP neural network has a high consistency with the visual evaluation, which will help for the automatic color fastness grading.
Keywords
textile fabrics; color fastness; digital grading; spectral reconstruction; BP neural network
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
