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

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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.

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Acknowledgements

This work was supported by JSPS KAKENHI Grant number 16K06203 and MITSUBISHIPENCIL CO., LTD.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering, Sanyo-Onoda City University, 1-1-1 Daigaku-Dori, Sanyo-Onoda, 756-0884, Japan

    Fusaomi Nagata, Kenta Tokuno, Kazuki Mitarai, Akimasa Otsuka, Takeshi Ikeda & Hiroaki Ochi

  2. Okayama University, Okayama, Japan

    Keigo Watanabe

  3. American University, Cairo, Egypt

    Maki K. Habib

Authors
  1. Fusaomi Nagata
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  2. Kenta Tokuno
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  3. Kazuki Mitarai
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  4. Akimasa Otsuka
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  5. Takeshi Ikeda
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  6. Hiroaki Ochi
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  7. Keigo Watanabe
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  8. Maki K. Habib
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Corresponding author

Correspondence to Fusaomi Nagata.

Additional information

This work was presented in part at the 24th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 23–25, 2019.

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Nagata, F., Tokuno, K., Mitarai, K. et al. Defect detection method using deep convolutional neural network, support vector machine and template matching techniques. Artif Life Robotics 24, 512–519 (2019). https://doi.org/10.1007/s10015-019-00545-x

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  • DOI: https://doi.org/10.1007/s10015-019-00545-x

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