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Volume: 32 | Article ID: art00006
Deep Learning for Printed Mottle Defect Grading
  DOI :  10.2352/ISSN.2470-1173.2020.8.IMAWM-184  Published OnlineJanuary 2020

In this paper, we propose a new method for printed mottle defect grading. By training the data scanned from printed images, our deep learning method based on a Convolutional Neural Network (CNN) can classify various images with different mottle defect levels. Different from traditional methods to extract the image features, our method utilizes a CNN for the first time to extract the features automatically without manual feature design. Different data augmentation methods such as rotation, flip, zoom, and shift are also applied to the original dataset. The final network is trained by transfer learning using the ResNet-34 network pretrained on the ImageNet dataset connected with fully connected layers. The experimental results show that our approach leads to a 13.16% error rate in the T dataset, which is a dataset with a single image content, and a 20.73% error rate in a combined dataset with different contents.

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Jianhang Chen, Qian Lin, Jan P. Allebach, "Deep Learning for Printed Mottle Defect Gradingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2020,  pp 184-1 - 184-9,

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