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