Earticle

초록

영어

Masonry is a common type of construction that uses mortar to bind individual units, such as brick or building stones, together to construct the structure. Even though masonry structures are durable, multiple factors such as the quality of mortar, workmanship, and harsh environment could greatly reduce the structural integrity, leading to defects and even human loss. Thus, it is crucial to perform the maintenance process regularly. Previously, the maintenance relied mainly on inspectors, who inspected the masonry structures to find cracks and determine the seriousness. However, this process is error-prone, costly, and time-consuming. As a result, this study proposes a fully automated masonry crack segmentation framework that robustly identifies various types of masonry cracks. In addition, the length of the segmentation cracks, which has been ignored in previous studies, is also computed.

목차

Abstract
I. INTRODUCTION
II. DATASET
III. METHODOLOGY
A. Deep Learning-based Crack Segmentation
B. Post-processing and Skeletonization
C. Crack Measurements
IV. EXPERIMENTAL RESULTS
V. CONCLUSION
REFERENCES

키워드

저자

• L. Minh Dang [ Department of Information and Communication Engineering, and Convergence Engineering for Intelligent Drone, Sejong University Seoul, Korea ]
• Le Quan Nguyen [ Department of Computer Science and Engineering, Sejong University Seoul, Korea ]
• Shin Jihye [ Department of Artificial Intelligence, Sejong University Seoul, Korea ]
• Hyeonjoon Moon [ Department of Computer Science and Engineering, Sejong University Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004