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    적응적 형태학적 분석에 기초한 신호등 인식률 성능 개선 = Performance Improvement of Traffic Signal Lights Recognition Based on Adaptive Morphological Analysis

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    https://www.riss.kr/link?id=A101787125

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Lots of research and development works have been actively focused on the self-driving vehicles, locally and globally. In order to implement the self-driving vehicles, lots of fundamental core technologies need to be successfully developed and, specially, it is noted that traffic lights detection and recognition system is an essential part of the computer vision technologies in the self-driving vehicles. Up to nowadays, most conventional algorithm for detecting and recognizing traffic lights are mainly based on the color signal analysis, but these approaches have limits on the performance improvements that can be achieved due to the color signal noises and environmental situations. In order to overcome the performance limits, this paper introduces the morphological analysis for the traffic lights recognition. That is, by considering the color component analysis and the shape analysis such as rectangles and circles simultaneously, the efficiency of the traffic lights recognitions can be greatly increased. Through several simulations, it is shown that the proposed method can highly improve the recognition rate as well as the mis-recognition rate.
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    Lots of research and development works have been actively focused on the self-driving vehicles, locally and globally. In order to implement the self-driving vehicles, lots of fundamental core technologies need to be successfully developed and, special...

    Lots of research and development works have been actively focused on the self-driving vehicles, locally and globally. In order to implement the self-driving vehicles, lots of fundamental core technologies need to be successfully developed and, specially, it is noted that traffic lights detection and recognition system is an essential part of the computer vision technologies in the self-driving vehicles. Up to nowadays, most conventional algorithm for detecting and recognizing traffic lights are mainly based on the color signal analysis, but these approaches have limits on the performance improvements that can be achieved due to the color signal noises and environmental situations. In order to overcome the performance limits, this paper introduces the morphological analysis for the traffic lights recognition. That is, by considering the color component analysis and the shape analysis such as rectangles and circles simultaneously, the efficiency of the traffic lights recognitions can be greatly increased. Through several simulations, it is shown that the proposed method can highly improve the recognition rate as well as the mis-recognition rate.

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    참고문헌 (Reference)

    1 김용권, "차세대 실감 내비게이션을 위한 실시간 신호등 및 표지판 객체 인식" 한국공간정보시스템학회 10 (10): 13-24, 2008

    2 김진수, "차량 장착 블랙박스 카메라를 이용한 효과적인 도로의 거리예측방법" 한국정보통신학회 19 (19): 651-658, 2015

    3 정준익, "성분차 색분할과 검출마스크를 통한실시간 교통신호등 검출과 인식" 대한전자공학회 43 (43): 65-72, 2006

    4 김선동, "디지털영상처리 기술을 이용한 교통신호등 자동 판별 시스템 개발" 대한전자공학회 46 (46): 92-99, 2009

    5 A. Lorsakul, "Traffic Sign Recognition for Intelligent Vehicle/Driver Assistance System Using Neural Network on OpenCV" 22-24, 2007

    6 M. Mathias, "Traffic Sign Recognition - How Far are We from the Solution" 2013

    7 J. Ryu, "Road Distance Estimation Based on Pinhole Model for a Vehicle-attached Black Box Camera" 2015

    8 R. Charette, "Real Time Visual Traffic Lights Recognition Based on Spot Light Detection and Adaptive Traffic Lights Templates" 2009

    9 M. Kim, "Implementation of Smart Car Infotainment System Including Black Box and Self-diagnosis Function" 8 (8): 267-274, 2014

    10 Y. Jie, "A New Traffic Light Detection and Recognition Algorithm for Electronic Travel Aid" 9-11, 2013

    1 김용권, "차세대 실감 내비게이션을 위한 실시간 신호등 및 표지판 객체 인식" 한국공간정보시스템학회 10 (10): 13-24, 2008

    2 김진수, "차량 장착 블랙박스 카메라를 이용한 효과적인 도로의 거리예측방법" 한국정보통신학회 19 (19): 651-658, 2015

    3 정준익, "성분차 색분할과 검출마스크를 통한실시간 교통신호등 검출과 인식" 대한전자공학회 43 (43): 65-72, 2006

    4 김선동, "디지털영상처리 기술을 이용한 교통신호등 자동 판별 시스템 개발" 대한전자공학회 46 (46): 92-99, 2009

    5 A. Lorsakul, "Traffic Sign Recognition for Intelligent Vehicle/Driver Assistance System Using Neural Network on OpenCV" 22-24, 2007

    6 M. Mathias, "Traffic Sign Recognition - How Far are We from the Solution" 2013

    7 J. Ryu, "Road Distance Estimation Based on Pinhole Model for a Vehicle-attached Black Box Camera" 2015

    8 R. Charette, "Real Time Visual Traffic Lights Recognition Based on Spot Light Detection and Adaptive Traffic Lights Templates" 2009

    9 M. Kim, "Implementation of Smart Car Infotainment System Including Black Box and Self-diagnosis Function" 8 (8): 267-274, 2014

    10 Y. Jie, "A New Traffic Light Detection and Recognition Algorithm for Electronic Travel Aid" 9-11, 2013

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    학술지 이력

    학술지 이력
    연월일 이력구분 이력상세 등재구분
    2027 평가 재인증평가 신청대상 (재인증)
    2021-01-01 등재 등재학술지 유지 (재인증) KCI등재
    2018-01-01 등재 등재학술지 선정 (계속평가) KCI등재
    2017-12-01 등재 등재후보로 하락 (계속평가) KCI등재후보
    2013-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2011-11-23 학술지명변경 외국어명 : THE JOURNAL OF The KOREAN Institute Of Maritime information & Communication Science -> Journal of the Korea Institute Of Information and Communication Engineering KCI등재
    2011-11-16 학회명변경 영문명 : International Journal of Information and Communication Engineering(IJICE) -> The Korea Institute of Information and Communication Engineering KCI등재
    2011-11-14 학회명변경 한글명 : 한국해양정보통신학회 -> 한국정보통신학회
    영문명 : 미등록 -> International Journal of Information and Communication Engineering(IJICE)
    KCI등재
    2010-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2008-01-01 등재 등재학술지 유지 (등재유지) KCI등재
    2005-01-01 등재 등재학술지 선정 (등재후보2차) KCI등재
    2004-01-01 등재 등재후보 1차 PASS (등재후보1차) KCI등재후보
    2002-07-01 등재 등재후보학술지 선정 (신규평가) KCI등재후보
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    학술지 인용정보

    학술지 인용정보
    기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
    2016 0.23 0.23 0.27
    KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
    0.24 0.22 0.424 0.11
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