An Hybrid Model CMR-Color of Automatic Color Matching Prediction for Textiles Dyeing and Printing

Abstract

Automatic color matching is an essential technology for dyeing and printing in the textiles industry to ensure customer quality standards and increase production efficiency. Computer-assisted automatic color matching has played an increasingly important role in textiles dyeing and printing as the cycle time of fashion decreases, with consumers constantly demanding new styles. Machine learning has been widely applied in various fields, however there are still difficulties such as the time-consuming and labor-intensive procedure of manual feature extraction required by conventional machine learning models. For dyeing and printing, the large amount of dyeing recipe data involving dye concentrations, spectra, and substrate materials have made it difficult to build artificial neural network models in complex data fitting. This paper proposes an automatic color matching prediction model, CMR-Color, by incorporating three neural network models including typical CNN, MLP, and ResNet to improve the capability of extracting high-dimensional features from spectral data. It selects the dyes required and respective concentration values of each component in the recipe. Moreover, it uses feature fusion to improve the prediction performance of automatic color matching. The experiment datasets of 72,132 recipes are from a world-class listed company in the textiles industry. The results show that the CMR-Color model achieves the best performance in all three evaluation metrics compared with four state-of-the-art baseline methods, verifying its effectiveness in color matching prediction for textiles dyeing and printing.