针对应用深度学习检测数码印花缺陷需准确分类的问题,提出了基于卷积神经网络(CNN)的数码印花缺陷分类算法。该方法首先依次对图像进行RGB颜色空间直方图均衡化、高斯滤波、局部均值分辨率调整的图像预处理,提升输入网络的图像质量,并进行图像几何变换的数据增强,扩充样本数据集;然后,设计拓扑结构为2个卷积层、2个池化层、2个全连接层的CNN网络对样本进行训练,得出最优的数码印花缺陷分类CNN模型。经600张测试样本验证,结果表明,该算法对各类数码印花缺陷的分类准确率均超过90.0%,多分类任务Kappa系数值为0.94,能实现数码印花缺陷的准确分类。

To accurately classify digital printing defects with deep learning, we propose a digital printing defect classification algorithm based on convolutional neural network (CNN). Firstly, this method performs image preprocessing of RGB color space histogram equalization, Gaussian filtering, and local mean resolution adjustment in sequence to improve the image quality of the input network. Meanwhile, the sample data set is expanded by geometrically transforming the image. Then, the topology of CNN network is designed with 2 convolutional layers, 2 pooling layers, and 2 fully connected layers, which is the optimized CNN model of digital printing defect classification. Finally, the model is verified by 600 test samples. Experimental results show that the classification accuracy of proposed algorithm for all types of digital printing defects reaches above 90.0%, and the Kappa coefficient value of multi-classification task is 0.94. The proposed method can accurately classify digital printing defects.