Design and implementation of convolutional neural network-based SVM technique for manufacturing defect detection

Fusaomi Nagata, Maki K. Habib, Keigo Watanabe

Research output: Contribution to journalArticlepeer-review

Abstract

This paper introduces the design, implementation, training and testing of deep convolutional neural network (DCNN)-based support vector machines (SVMs). These DCNN-based SVMs are designed using software tools developed by the authors that enable them to construct, train, and test the DCNN-based SVMs and effectively facilitate vision-based inspection to detect different undesirable manufacturing defects. Two pretrained DCNNs are used for this purpose: the sssNet is developed by the authors and was trained using many actual and simple target images consisting of seven categories, and the standard AlexNet that was trained by a large number of images consisting of 1,000 categories. In this work, the pretrained sssNet and AlexNet are used as feature vector extractors in training and testing. The generated feature vectors are used as inputs to train SVMs for the final binary classification represented as accept (OK) or reject (NG) category.

Original languageEnglish
Pages (from-to)53-61
Number of pages9
JournalInternational Journal of Mechatronics and Automation
Volume8
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • CNN
  • Convolutional neural network
  • Defect inspection system
  • Edge extraction
  • One-class learning of SVM
  • Support vector machine
  • SVM
  • Template matching
  • Two-class learning of SVM

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Mechanics
  • Industrial and Manufacturing Engineering
  • Computational Mathematics
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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