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Open AccessArticle

A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

by Erhu Zhang 1,2,*, Bo Li 1, Peilin Li 1 and Yajun Chen 1,2
1
Department of Information Science, Xi’an University of Technology, Xi’an 710048, China
2
Shaanxi Provincial Key Laboratory of Printing and Packaging Engineering, Xi’an University of Technology, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(12), 1440; https://doi.org/10.3390/sym11121440 (registering DOI)
Received: 6 November 2019 / Revised: 20 November 2019 / Accepted: 21 November 2019 / Published: 22 November 2019
Deep learning has been successfully applied to classification tasks in many fields due to its good performance in learning discriminative features. However, the application of deep learning to printing defect classification is very rare, and there is almost no research on the classification method for printing defects with imbalanced samples. In this paper, we present a deep convolutional neural network model to extract deep features directly from printed image defects. Furthermore, considering the asymmetry in the number of different types of defect samples—that is, the number of different kinds of defect samples is unbalanced—seven types of over-sampling methods were investigated to determine the best method. To verify the practical applications of the proposed deep model and the effectiveness of the extracted features, a large dataset of printing detect samples was built. All samples were collected from practical printing products in the factory. The dataset includes a coarse-grained dataset with four types of printing samples and a fine-grained dataset with eleven types of printing samples. The experimental results show that the proposed deep model achieves a 96.86% classification accuracy rate on the coarse-grained dataset without adopting over-sampling, which is the highest accuracy compared to the well-known deep models based on transfer learning. Moreover, by adopting the proposed deep model combined with the SVM-SMOTE over-sampling method, the accuracy rate is improved by more than 20% in the fine-grained dataset compared to the method without over-sampling. View Full-Text
Keywords: deep convolutional neural network; printing defect classification; feature extraction; imbalance learning deep convolutional neural network; printing defect classification; feature extraction; imbalance learning
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Zhang, E.; Li, B.; Li, P.; Chen, Y. A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples. Symmetry 2019, 11, 1440.

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