Defect detection method using deep convolutional neural network, support vector machine and template matching techniques

Fusaomi Nagata, Kenta Tokuno, Kazuki Mitarai, Akimasa Otsuka, Takeshi Ikeda, Hiroaki Ochi, Keigo Watanabe, Maki K. Habib

Research output: Contribution to journalArticle

Abstract

In this paper, a defect detection method using deep convolutional neural network (DCNN), support vector machine (SVM) and template matching techniques is introduced. First, a DCNN for visual inspection is designed and trained using a large number of images to inspect undesirable defects such as crack, burr, protrusion, chipping, spot and fracture phenomena which appear in the manufacturing process of resin molded articles. Then the trained DCNN named sssNet and well-known AlexNet are, respectively, incorporated with two SVMs to classify sample images with high recognition rate into accept as OK category or reject as NG one, in which compressed feature vectors obtained from the DCNNs are used as inputs for the SVMs. The performances of the two types of SVMs with the DCNNs are compared and evaluated through training and classification experiments. Finally, a template matching technique is further proposed to efficiently extract important target areas from original training and test images. This will be able to enhance the reliability and accuracy for defect detection.

Original languageEnglish
Pages (from-to)512-519
Number of pages8
JournalArtificial Life and Robotics
Volume24
Issue number4
DOIs
Publication statusPublished - Dec 1 2019

Keywords

  • Deep convolutional neural network (DCNN)
  • Defect detection system
  • Support vector machine (SVM)
  • Template matching

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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