Defect Classification of Electronic Board Using Dense SIFT and CNN

This paper proposes a new defect classification method of electronic board using Dense SIFT and CNN which can represent the effective features to the gray scale image. Proposed method does not use any reference image and effective keypoints are detected using Dense SIFT on the defect candidate regio...

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Bibliographic Details
Published inProcedia computer science Vol. 126; pp. 1673 - 1682
Main Authors Iwahori, Yuji, Takada, Yohei, Shiina, Tokiko, Adachi, Yoshinori, Bhuyan, M.K., Kijsirikul, Boonserm
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2018
Subjects
Bag of Features
Convolutional Neural Network
Defect Classification
Support Vector Machine
Convolutional Neural Network
Support Vector Machine
Defect Classification
Bag of Features
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Summary:This paper proposes a new defect classification method of electronic board using Dense SIFT and CNN which can represent the effective features to the gray scale image. Proposed method does not use any reference image and effective keypoints are detected using Dense SIFT on the defect candidate region. Removing the feature points except defect region and Bag of Features are used to represent the histogram features. Dense SIFT and SVM are used to judge defect or not. CNN is further introduced to classify true or pseudo defect. Classification accuracy was evaluated and effectiveness of the proposed method is shown.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2018.08.110