Convolutional neural network for industrial egg classification

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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    10 Citations (Scopus)


    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.

    Original languageEnglish
    Title of host publicationProceedings - International SoC Design Conference 2017, ISOCC 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages2
    ISBN (Electronic)9781538622858
    Publication statusPublished - 2018 May 29
    Event14th International SoC Design Conference, ISOCC 2017 - Seoul, Korea, Republic of
    Duration: 2017 Nov 52017 Nov 8

    Publication series

    NameProceedings - International SoC Design Conference 2017, ISOCC 2017


    Other14th International SoC Design Conference, ISOCC 2017
    Country/TerritoryKorea, Republic of


    • Convolutional Neural Network
    • Image Classification
    • Quality Inspection
    • Small-scale Dataset

    ASJC Scopus subject areas

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


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