Digital Printing Defect Classification Algorithm Based on Convolutional Neural Network

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 RGH 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 0.91. The proposed method can accurately classify digital printing defects.