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Paper Defects Classification Based on Multi-scale Image Enhancement Combined with Convolution Neural Network
  
DOI：10.11980/j.issn.0254-508X.2018.08.009
Key Words:image enhancement  convolution neural network（CNN）  multi-scale morphological gradient  image classification
Fund Project:国家自然科学基金（61461025）；国家自然科学基金（61811530325）；中国博士后科学基金项目（2016M602856）。
Author NameAffiliation
LI Guang-ming College of Electrical and Information Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 
XUE Ding-hua College of Electrical and Information Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 
JIA Xiao-hong College of Electrical and Information Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 
LI Yun-tong College of Electrical and Information Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 
LEI Tao* College of Electrical and Information Engineering， Shaanxi University of Science and Technology， Xi’an， Shaanxi Province， 710021 
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Abstract:In paper industry， traditional approaches of paper defects classification depend on the selection of feature descriptors and the selector.To address the problem， an approach of paper defects classification based on multi-scale image enhancement combined with convolution neural network(CNN） was proposed in this paper-Firstly， multi-scale morphological gradient was computed and used to enhance the edge contour information of paper image， the gradient features of defects in a paper image was highlighted.Then， CNN was applied to learn image features and employed to classify paper defects images.Finally， paper defects classification with a high accuracy was achieved.The experimental results showed that the proposed approach obtained a higher accuracy， 98.44%， 99.23%， and 99.64% on Caltech101， KTH-TIPS， and our datasets， respectively， was significantly superior to the methods of HOG+SVM， LBP+SVM， and traditional CNN.Compared to current methods for paper defects classification， the proposed approach did not require to extract defects features using feature descriptors．
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