Design Tool of Deep Convolutional Neural Network for Intelligent Visual Inspection

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

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


Recently, convolutional neural networks (CNNs) are used essentially to classify images as it helps to cluster them by similarity and perform recognition. In this paper, a design tool that helps to develop different deep CNNs (DCNNs) is presented. As an example, a DCNN is designed by using the developed tool to use it for vision based inspection to recognize undesirable defects such as crack, burr, protrusion and chipping which normally occur in the manufacturing process of resin molded articles. An image generator is implemented to efficiently produce many similar images for training. Similar images are easily generated by rotating, translating, scaling and transforming original images. The designed DCNN is trained by using the produced images and then tested through classification experiments. The usefulness of the design tool and the basic performance of the designed DCNN are introduced.

Original languageEnglish
Article number012073
JournalIOP Conference Series: Materials Science and Engineering
Issue number1
Publication statusPublished - Nov 6 2018
Event2018 4th International Conference on Applied Materials and Manufacturing Technology, ICAMMT 2018 - Nanchang, China
Duration: May 25 2018May 27 2018

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)


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