Precise dyeing recipe prediction is important in the final colour reproduction of textile dyeing and printing products. Currently, the widely used dyeing recipe prediction methods based on colour tri‐stimulus cannot effectively avoid the metamerism phenomenon. An intelligent dyeing recipe prediction model for cotton fabric dyeing is proposed in this paper based on hyperspectral colour measurement and a deep learning algorithm. The hyperspectral colour measurement can obtain three‐dimensional spectral information (X, Y and λ) of fabric samples, and can acquire accurate colour values even with uneven samples if the regional correlation algorithm is used. A deep learning algorithm based on an improved recurrent neural network was then employed to establish the model between spectral reflectance and the dyeing recipe. In total, 343 evenly dyed and 20 unevenly dyed fabric samples were dyed using the dyestuffs of Reactive Red CI 238, Reactive Blue CI 204 and Reactive Yellow CI 206, upon which the recipe prediction model was based, established and evaluated. The experimental results show that the proposed model based on hyperspectral colour measurement and our algorithm can provide higher prediction accuracy for Reactive Red CI 238, Reactive Blue CI 204 and Reactive Yellow CI 206. The relative prediction errors are 3.40%, 2.70% and 3.10%, respectively, for these three types of dyeing recipe, while the relative prediction errors are 19.60%, 22.60% and 11.83%, respectively, using the Datacolor 650 recipe prediction model.

中文翻译：

---

基于高光谱颜色测量和改进的递归神经网络的棉织物染色配方预测

精确的染色配方预测对于纺织品染色和印花产品的最终色彩再现非常重要。目前，基于颜色三刺激的广泛使用的染色配方预测方法无法有效避免同色异谱现象。提出了一种基于高光谱测色和深度学习算法的棉织物染色智能化配方预测模型。如果使用区域相关算法，则高光谱颜色测量可以获取织物样本的三维光谱信息（X，Y和λ），并且即使在不均匀样本的情况下也可以获取准确的颜色值。然后，基于改进的递归神经网络的深度学习算法被用于建立光谱反射率和染色配方之间的模型。总共，使用活性红CI 238，活性蓝CI 204和活性黄CI 206的染料对343个均匀染色的织物样品和20个不均匀染色的织物样品进行染色，并以此为基础建立，评估了配方预测模型。实验结果表明，所提出的基于高光谱色彩测量的模型和我们的算法可以为活性红CI 238，活性蓝CI 204和活性黄CI 206提供更高的预测精度。相对预测误差为3.40％，2.70％和3.10％。对于这三种类型的染色配方，使用Datacolor 650配方预测模型的相对预测误差分别为19.60％，22.60％和11.83％。配方预测模型基于，建立和评估。实验结果表明，所提出的基于高光谱色彩测量的模型和我们的算法可以为活性红CI 238，活性蓝CI 204和活性黄CI 206提供更高的预测精度。相对预测误差为3.40％，2.70％和3.10％。对于这三种染色配方，使用Datacolor 650配方预测模型的相对预测误差分别为19.60％，22.60％和11.83％。配方预测模型基于，建立和评估。实验结果表明，所提出的基于高光谱色彩测量的模型和我们的算法可以为活性红CI 238，活性蓝CI 204和活性黄CI 206提供更高的预测精度。相对预测误差为3.40％，2.70％和3.10％。对于这三种染色配方，使用Datacolor 650配方预测模型的相对预测误差分别为19.60％，22.60％和11.83％。