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Deep learning-based masonry crack segmentation and real-life crack length measurement

Minh Dang Department of Computer Science and Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006, South Korea|
Hyeonjoon (23397814300) | Hyoung-Kyu (57722863000); Moon | Tan N. (57192179687); Song | Le Quan (57861788000); Nguyen | Yanfen (57218425855); Nguyen Department of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006, South Korea| Hanxiang (57212682280); Li Department of Information Technology, FPT University, Ho Chi Minh city, 70000, Viet Nam| L. (57217175339); Wang Department of Information and Communication Engineering, Convergence Engineering for Intelligent Drone, Sejong University, Seoul, South Korea|

Construction and Building Materials Số , năm 2022 (Tập 359, trang -)

ISSN: 9500618

ISSN: 9500618

DOI: 10.1016/j.conbuildmat.2022.129438

Tài liệu thuộc danh mục:



Từ khóa: Crack detection; Image segmentation; Large dataset; Masonry construction; Masonry materials; Walls (structural partitions); Computer vision techniques; Crack length measurement; Crack segmentations; Deep learning; Images processing; Masonry building; Masonry walls; Measurements of; Public facilities; Vision based; Deep learning
Tóm tắt tiếng anh
While there have been a considerable number of studies on computer vision (CV)-based crack detection on concrete/asphalt public facilities, such as sewers and tunnels, masonry-related structures have received less attention. This research seeks to implement an automated crack segmentation and a real-life crack length measurement of masonry walls using CV techniques and deep learning. The main contributions include (1) a large dataset of manually labelled images about various types of Korea masonry walls; (2) a careful performance evaluation of various deep learning-based crack segmentation models, including U-Net, DeepLabV3+, and FPN; and (3) a novel algorithm to extract real-life crack length measurement by detecting the brick units. The experimental results showed that deep learning-based masonry crack segmentation performed significantly better than previous approaches and could provide a real-life crack measurement. Therefore, it has a huge potential for motivating masonry-based structure investigation. � 2022 Elsevier Ltd

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