Convolutional neural network for industrial egg classification

Ryota Shimizu, Shusuke Yanagawa, Toru Shimizu, Mototsugu Hamada, Tadahiro Kuroda

    研究成果: Conference contribution

    2 被引用数 (Scopus)

    抄録

    CNN (Convolutional Neural Network) is a powerful method for image classifying tasks. CNN's classifying capability is assessed by using large-scale image dataset such as ImageNet in many papers, but few works on CNN with small-scale dataset have been reported. We have been researching application method of Neural Network for classifying tasks in real-world for years [1]. In this work, we applied CNN to a quality inspection of industrial products and assessed its classifying capacity. Our CNN was trained with 2000 images of eggs taken in a factory, classified the images of almost 89,000 eggs into 6 qualities. Our method of combining multi-angle images into 1 image retained the 3-dimensional features of the object, and improved the classification accuracy to 92.3%. It confirmed that CNN is also effective for the quality inspection of industrial products.

    本文言語English
    ホスト出版物のタイトルProceedings - International SoC Design Conference 2017, ISOCC 2017
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ67-68
    ページ数2
    ISBN（電子版）9781538622858
    DOI
    出版ステータスPublished - 2018 5 29
    イベント14th International SoC Design Conference, ISOCC 2017 - Seoul, Korea, Republic of
    継続期間: 2017 11 5 → 2017 11 8

    Other

    Other14th International SoC Design Conference, ISOCC 2017
    CountryKorea, Republic of
    CitySeoul
    Period17/11/5 → 17/11/8

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Electrical and Electronic Engineering
    • Electronic, Optical and Magnetic Materials

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