Detection of minute defects using transfer learning-based CNN models

Kento Nakashima, Fusaomi Nagata, Hiroaki Ochi, Akimasa Otsuka, Takeshi Ikeda, Keigo Watanabe, Maki K. Habib

Research output: Contribution to journalArticle

Abstract

In this paper, a design and training tool for convolutional neural networks (CNNs) is introduced, which facilitates to construct transfer learning-based CNNs based on a series-type network such as AlexNet, VGG16 and VGG19 or a directed acyclic graph (DAG)-type network such as GoogleNet, Inception-v3 and IncResNetV2. Minute defect detection systems are developed for resin-molded articles by transfer learning of AlexNet. AlexNet has the shallowest layer structure and the smallest number of weights within the six powerful networks, so that it is selected as the first CNN for evaluation. In the transfer learning process, after the last fully connected layers are replaced according to the number of categories needed for new tasks, an additional fine training is conducted using training images including small typical defects. In experiments, transfer learning-based AlexNet_6 and AlexNet_2 are obtained to deal with six and binary classification tasks, respectively. Then, our originally designed 15 layers CNNs named sssNet_6 and sssNet_2 are also prepared and trained for comparison. Finally, AlexNet_6 and sssNet_6, AlexNet_2 and sssNet_2 are quantitatively compared and evaluated through classification experiments, respectively.

Original languageEnglish
JournalArtificial Life and Robotics
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • CNN design tool
  • Convolutional neural network (CNN)
  • Minute defect detection
  • Transfer learning

ASJC Scopus subject areas

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

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