Deep learning-based masonry crack segmentation and real-life crack length measurement. (12th December 2022)

• Record Type: Journal Article
• Authors: Minh Dang, L.; Wang, Hanxiang; Li, Yanfen; Nguyen, Le Quan; Nguyen, Tan N.; Song, Hyoung-Kyu; Moon, Hyeonjoon
• Is Part Of: Construction & building materials. Volume 359(2022)
• Publication Date: 2022-12-12
• Subjects: Masonry building -- Crack segmentation -- Deep learning -- Measurement -- Image processing; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2022.129438
• Languages: English
• ISSNs: 0950-0618

Abstract

Graphical abstract: Highlights: A manually collected masonry crack segmentation dataset that contains approximately 2670 samples. An efficient DeepLabV3-based masonry crack segmentation framework. Actual length measurement of the segmented cracks based on brick unit's size. The proposed model outperformed previous state-of-the-art crack segmentation models. Abstract: 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.