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      스마트폰을 이용한 콘크리트 구조물의 최대 균열 폭 측정 시스템 개발 = Development of the Maximum Crack Width Measurement System Using Smartphones in Concrete Structures

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      https://www.riss.kr/link?id=T15521830

      • 저자
      • 발행사항

        부산 : 동의대학교 대학원, 2019

      • 학위논문사항

        학위논문(석사) -- 동의대학교 대학원 , 토목공학과 , 2020. 2

      • 발행연도

        2019

      • 작성언어

        한국어

      • 발행국(도시)

        부산

      • 형태사항

        62 ; 26 cm

      • 일반주기명

        지도교수: 정범석

      • UCI식별코드

        I804:21010-200000298418

      • 소장기관
        • 동의대학교 중앙도서관 소장기관정보
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      부가정보

      국문 초록 (Abstract)

      본 논문의 목적은 영상처리기법을 안드로이드와 적용하여 아두이노를 통해 콘크리트 구조물의 최대 균열 폭 측정 시스템을 개발하는 것이다. 현재콘크리트 구조물의 균열 폭은 육안검사와 균열게이지 등의 계측 장비로 측정되고 있다. 육안검사의 경우는 특정 조사자에 따라 결과값이 달라지므로 신뢰성이 다소 미흡하며, 균열게이지와 같은 계측 장비는 공간과 시간적 제약을 받는다. 이를 위하여 영상처리기법을 통해 신뢰성 있는 결과값을 측정하고자 하였으며, 스마트폰을 사용함으로써 실시간으로 측정이 가능하고 편리한 균열 폭 측정 시스템을 개발하였다.
      영상처리기법에서는 Bilateral Blur와 Adaptive Threshold를 사용하여 영상의 잡음과 빛에 대한 보정을 하였으며, 잡음이 제거된 영상은 Laplacian기법을 사용하여 균열의 외곽선을 추출하였다. 균열의 외곽선에서 최대 균열 픽셀 수는 x축과 y축에서 삼각비를 이용하여 산정하였으며, Reference Bar를 이용하여 Scale Factor를 계산하고, 픽셀단위로 계산된 최대 균열 픽셀 수를 mm단위로 환산하였다.
      본 논문에서 제안한 시스템은 균열게이지로 계측된 균열 폭과 비교분석하였으며, 균열 폭 평가에 대한 신뢰성 있는 결과를 도출할 수 있을 것으로 판단된다.
      번역하기

      본 논문의 목적은 영상처리기법을 안드로이드와 적용하여 아두이노를 통해 콘크리트 구조물의 최대 균열 폭 측정 시스템을 개발하는 것이다. 현재콘크리트 구조물의 균열 폭은 육안검사와 ...

      본 논문의 목적은 영상처리기법을 안드로이드와 적용하여 아두이노를 통해 콘크리트 구조물의 최대 균열 폭 측정 시스템을 개발하는 것이다. 현재콘크리트 구조물의 균열 폭은 육안검사와 균열게이지 등의 계측 장비로 측정되고 있다. 육안검사의 경우는 특정 조사자에 따라 결과값이 달라지므로 신뢰성이 다소 미흡하며, 균열게이지와 같은 계측 장비는 공간과 시간적 제약을 받는다. 이를 위하여 영상처리기법을 통해 신뢰성 있는 결과값을 측정하고자 하였으며, 스마트폰을 사용함으로써 실시간으로 측정이 가능하고 편리한 균열 폭 측정 시스템을 개발하였다.
      영상처리기법에서는 Bilateral Blur와 Adaptive Threshold를 사용하여 영상의 잡음과 빛에 대한 보정을 하였으며, 잡음이 제거된 영상은 Laplacian기법을 사용하여 균열의 외곽선을 추출하였다. 균열의 외곽선에서 최대 균열 픽셀 수는 x축과 y축에서 삼각비를 이용하여 산정하였으며, Reference Bar를 이용하여 Scale Factor를 계산하고, 픽셀단위로 계산된 최대 균열 픽셀 수를 mm단위로 환산하였다.
      본 논문에서 제안한 시스템은 균열게이지로 계측된 균열 폭과 비교분석하였으며, 균열 폭 평가에 대한 신뢰성 있는 결과를 도출할 수 있을 것으로 판단된다.

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      다국어 초록 (Multilingual Abstract)

      The purpose of this paper is to develop a system for measuring the maximum crack width of concrete structures through aduino by applying image processing techniques with Android. Currently, the width of cracks in concrete structures is measured by visual inspection and measuring equipment such as crack gauges. In the case of visual inspection, the reliability of the results varies depending on the specific investigator, and measurement equipment such as crack gauges are subject to space and time constraints. For this purpose, consistent reliable results were measured through the image processing method, and a convenient crack measuring system was developed for real-time measurement by using a smartphone.
      In the image processing technique, the noise and light of the images were corrected using the Bilateral Blur and Adaptive Threshold, and the images with noise removed were extracted with Laplacian technique. The maximum number of crack pixels on the outline of a crack was calculated using a triangular ratio on the x-axis and y-axis, and the Scale Factor was calculated using the Reference Bar, and the maximum number of crack pixels calculated in pixels was converted to mm.
      The system proposed in this paper has been compared and analyzed with the crack widths measured with crack gauges, and it is judged that reliable results can be derived for crack width evaluation.
      번역하기

      The purpose of this paper is to develop a system for measuring the maximum crack width of concrete structures through aduino by applying image processing techniques with Android. Currently, the width of cracks in concrete structures is measured by vis...

      The purpose of this paper is to develop a system for measuring the maximum crack width of concrete structures through aduino by applying image processing techniques with Android. Currently, the width of cracks in concrete structures is measured by visual inspection and measuring equipment such as crack gauges. In the case of visual inspection, the reliability of the results varies depending on the specific investigator, and measurement equipment such as crack gauges are subject to space and time constraints. For this purpose, consistent reliable results were measured through the image processing method, and a convenient crack measuring system was developed for real-time measurement by using a smartphone.
      In the image processing technique, the noise and light of the images were corrected using the Bilateral Blur and Adaptive Threshold, and the images with noise removed were extracted with Laplacian technique. The maximum number of crack pixels on the outline of a crack was calculated using a triangular ratio on the x-axis and y-axis, and the Scale Factor was calculated using the Reference Bar, and the maximum number of crack pixels calculated in pixels was converted to mm.
      The system proposed in this paper has been compared and analyzed with the crack widths measured with crack gauges, and it is judged that reliable results can be derived for crack width evaluation.

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      목차 (Table of Contents)

      • 요약 ······························································································································· ⅰ
      • 목차 ······························································································································· ⅱ
      • 그림목록(List of figures) ························································································ ⅲ
      • 표목록(List of Table) ······························································································· ⅳ
      • 1. 서론 ··························································································································· 1
      • 요약 ······························································································································· ⅰ
      • 목차 ······························································································································· ⅱ
      • 그림목록(List of figures) ························································································ ⅲ
      • 표목록(List of Table) ······························································································· ⅳ
      • 1. 서론 ··························································································································· 1
      • 1.1 연구의 배경 ······································································································· 1
      • 1.2 관련 연구의 이해 ····························································································· 2
      • 1.3 연구목적 및 연구범위 ····················································································· 5
      • 2. 이론적 배경 ············································································································· 7
      • 2.1 영상처리기법 ····································································································· 7
      • 2.1.1 양방향 블러 ······························································································· 7
      • 2.1.2 적응형 경계 이진화 ················································································ 12
      • 2.1.3 라플라시안 필터 ····················································································· 13
      • 2.1.4 최대 균열 폭 평가 ················································································· 19
      • 2.2 안드로이드 ······································································································· 22
      • 2.2.1 모바일 운영체제 ····················································································· 22
      • 2.2.2 안드로이드에 대한 이해 ········································································ 24
      • 2.2.3 안드로이드의 특징 및 구조 ·································································· 25
      • 2.2.4 안드로이드 애플리케이션의 기초 ························································ 27
      • 2.3 아두이노 ··········································································································· 28
      • 2.3.1 아두이노의 탄생과 배경 ········································································ 28
      • 2.3.2 아두이노에 대한 이해 ············································································ 28
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