Earticle

초록

영어

Masonry structures account for a large proportion of the building stock worldwide. Presently, the structural conditions of such structures are mostly inspected manually, and which is expensive, laborious and subjective processes. As deep learning technique for computer vision advances, there is an opportunity to automate the visual inspection process using digital images. Several studies are in progress to automatically detect cracks in masonry structures using Deep Learning. However, it is important not only detecting a crack, but also measuring a length of the crack. This is because it is necessary to consider various factors required in the actual environment, such as calculating the cost of reinforcement work. In this paper, we propose the method that detects masonry cracks and measures the length of cracks with digital images. The aim of this study is to implement Deep Learning model for crack detection on masonry structure and to apply the method of crack length measurement additionally.

목차

Abstract
I. INTRODUCTION
II. MATERIALS AND METHODS
A. Proposed overall framework
B. Proposed model and methods
C. Dataset for training and testing the model
III. EXPERIMENT AND RESULT
A. Metrics for evaluation of the model
B. Experiment environments
C. Results of the model
D. Results of the proposed framework
IV. CONCLUSION
REFERENCES

키워드

저자

• Hanil Na [ Department of Computer and Engineering Sejong university ]
• Seonbin Choi [ Department of Computer and Engineering Sejong university ]
• Yong Nam Kim [ Mirae Structural Engineers Seoul, Korea ]
• Ki-Hak Lee [ Department of Architectural Engineering Sejong university ]
• Hyeonjoon Moon [ Department of Computer and Engineering Sejong university ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004