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    Vision Sensor Technology Trends for Industrial Inspection System

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    https://www.riss.kr/link?id=A107938363

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    The fourth industrial revolution is rapidly emerging as a new innovation trend for industrial automation. Accordingly, the demand for inspection equipment is highly increasing and vision sensor technologies are continuously evolving. Machine vision algorithms applied to deep learning are also being rapidly developed to maximize the performance of inspection equipment. In this review, we highlight the recent progress of vision sensor technology for the industrial inspection system. In particular, inspection principles and industrial applications of a vision sensor are classified according to the vision scanning methods. We also discuss machine vision-based inspection techniques containing rule- and deep learning-based image processing algorithms. We believe that this review provides novel approaches for various inspection fields of agriculture, medicine, and manufacturing industries.
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    The fourth industrial revolution is rapidly emerging as a new innovation trend for industrial automation. Accordingly, the demand for inspection equipment is highly increasing and vision sensor technologies are continuously evolving. Machine vision al...

    The fourth industrial revolution is rapidly emerging as a new innovation trend for industrial automation. Accordingly, the demand for inspection equipment is highly increasing and vision sensor technologies are continuously evolving. Machine vision algorithms applied to deep learning are also being rapidly developed to maximize the performance of inspection equipment. In this review, we highlight the recent progress of vision sensor technology for the industrial inspection system. In particular, inspection principles and industrial applications of a vision sensor are classified according to the vision scanning methods. We also discuss machine vision-based inspection techniques containing rule- and deep learning-based image processing algorithms. We believe that this review provides novel approaches for various inspection fields of agriculture, medicine, and manufacturing industries.

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    참고문헌 (Reference)

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    8 Jiang, J., "Surface Defect Detection for Mobile Phone Back Glass based on Symmetric Convolutional Neural Network Deep Learning" 10 (10): 2020

    9 Clancy, N. T., "Spectrally Encoded Fiber-Based Structured Lighting Probe for Intraoperative 3D Imaging" 2 (2): 3119-3128, 2011

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    55 Fu, G., "A DeepLearning-Based Approach for Fast and Robust Steel Surface Defects Classification" 121 : 397-405, 2019

    56 Yuan-Fu, Y., "A Deep Learning Model for Identification of Defect Patterns in Semiconductor Wafer Map" 1-6, 2019

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    58 Raytrix, "3D Light-Field Vision, Depth Sensors, Plenoptic Metrology"

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2023 평가 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
    2020-01-01 등재 등재학술지 유지 (해외등재 학술지 평가) KCI등재
    2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2008-06-23 학회명변경 영문명 : Korean Society Of Precision Engineering -> Korean Society for Precision Engineering KCI등재
    2008-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2006-07-07 학술지명변경 외국어명 : 미등록 -> Journal of the Korean Society for Precision Engineering KCI등재
    2006-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2004-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2001-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    1998-07-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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    학술지 인용정보

    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 0.26 0.26 0.26
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    0.24 0.22 0.449 0.12
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