Liang Jinxing, Zhou Jing, Hu Xinrong, Luo Hang, Cao Genyang, Liu Liu, Xiao Kaida
School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China.
School of Automation, Qingdao University, Qingdao 266071, China.
J Imaging. 2023 Nov 16;9(11):251. doi: 10.3390/jimaging9110251.
To digital grade the staining 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 gray scale difference method based on the five-fold cross-validation strategy. Experiments show that the prediction model of fabric color fastness can be better constructed using the BP neural network. The overall performance of the method is better than the color difference conversion method and the gray scale difference method. It can be seen that the digital rating method of fabric staining color fastness to rubbing based on spectral reconstruction and BP neural network has high consistency with the visual evaluation, which will help for the automatic color fastness grading.
为对织物摩擦后的染色色牢度进行数字分级，提出了一种基于光谱重建技术和BP神经网络的自动分级方法。首先，按照ISO 105-X12标准对织物进行摩擦制备建模样本。然后，为符合色牢度的视觉评级标准，对建模样本进行专业分级以获得视觉评级结果。之后，在封闭且光照均匀的箱体内使用数码相机拍摄建模样本的数字图像，并通过光谱重建技术获取建模样本的颜色数据值。最后，利用建模样本数据和BP神经网络构建摩擦色牢度预测模型。使用该预测模型预测测试样本的色牢度等级，并基于五折交叉验证策略将预测结果与现有的色差转换方法和灰度差方法进行比较。实验表明，利用BP神经网络能够更好地构建织物色牢度预测模型。该方法的整体性能优于色差转换方法和灰度差方法。可见，基于光谱重建和BP神经网络的织物摩擦染色色牢度数字评级方法与视觉评价具有高度一致性，这将有助于色牢度的自动分级。
