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Development of a Severity Level Decision Making Process of Road Problems and Its Application Analysis using Deep Learning
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딥러닝을 이용한 도로 문제점의 심각도 판단기법 개발 및 적용사례 분석

논문 기본 정보

Type
Academic journal
Author
Woo Hoon Jeon (한국건설기술연구원) Inchul Yang (한국건설기술연구원) Joyoung Lee (뉴저지 공과대학교)
Journal
The Korea Contents Society JOURNAL OF THE KOREA CONTENTS ASSOCIATION Vol.22 No.10 KCI Accredited Journals
Published
2022.10
Pages
535 - 545 (11page)

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Result
Development of a Severity Level Decision Making Process of Road Problems and Its Application Analysis using Deep Learning
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Abstract· Keywords

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The purpose of this study is to classify the various problems in surface road according to their severity and to propose a priority decision making process for road policy makers. For this purpose, the road problems reported by Cheok-cheok app were classified, and the EPDO was adopted and calculated as an index of their severity. To test applicability of the proposed process, some images of road problems reported by the app were classified and annotated, and the Deep Learning was used for machine learning of the curated images, and then the other images of road problems were used for verification. The detecting success rate of the road problems with high severity such as road kills, obstacles in a lane, road surface cracks was over 90%, which shows the applicability of the proposed process. It is expected that the proposed process will make the app possible to be used in the filed to make a priority decision making by classifying the level of severity of the reported road problems automatically.
#도로불편 #심각도 #딥러닝 #객체 인식 #Road Problem #Severity #EPDO #Deep Learning #Recognition

Contents

요약
Abstract
Ⅰ. 서론
Ⅱ. 선행연구 고찰
Ⅲ. 도로 문제점의 심각도 판단기법 개발
Ⅲ. 문제 심각도 판단기법 적용 사례
Ⅳ. 결론 및 향후연구
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