CNN 모델을 활용한 콘크리트 균열 검출 및 시각화 방법Concrete Crack Detection and Visualization Method Using CNN Model
- Other Titles
- Concrete Crack Detection and Visualization Method Using CNN Model
- Authors
- 최주희; 김영관; 이한승
- Issue Date
- Apr-2022
- Publisher
- 한국건축시공학회
- Keywords
- 콘크리트균열; 딥러닝; 시각화; concrete crack; deep learning; visualization
- Citation
- 한국건축시공학회 2022 봄학술발표대회 논문집, v.22, no.1, pp 73 - 74
- Pages
- 2
- Indexed
- OTHER
- Journal Title
- 한국건축시공학회 2022 봄학술발표대회 논문집
- Volume
- 22
- Number
- 1
- Start Page
- 73
- End Page
- 74
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114033
- Abstract
- Concrete structures occupy the largest proportion of modern infrastructure, and concrete structures often have cracking problems. Existing concrete crack diagnosis methods have limitations in crack evaluation because they rely on expert visual inspection. Therefore, in this study, we design a deep learning model that detects, visualizes, and outputs cracks on the surface of RC structures based on image data by using a CNN (Convolution Neural Networks) model that can process two- and three-dimensional data such as video and image data. do. An experimental study was conducted on an algorithm to automatically detect concrete cracks and visualize them using a CNN model. For the three deep learning models used for algorithm learning in this study, the concrete crack prediction accuracy satisfies 90%, and in particular, the ‘InceptionV3’-based CNN model showed the highest accuracy. In the case of the crack detection visualization model, it showed high crack detection prediction accuracy of more than 95% on average for data with crack width of 0.2 mm or more.
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