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2023 (English)In: Journal of Civil Structural Health Monitoring, ISSN 2190-5452, Vol. 13, no 8, p. 1633-1652Article in journal (Refereed) Published
Abstract [en]
Bridge inspections are relied heavily on visual inspection, and usually conducted within limited time windows, typically at night, to minimize their impact on traffic. This makes it difficult to inspect every meter of the structure, especially for large-scale bridges with hard-to-access areas, which creates a risk of missing serious defects or even safety hazards. This paper presents a new technique for the semi-automated damage detection in tunnel linings and bridges using a hybrid approach based on photogrammetry and deep learning. The first approach involves using photogrammetry to reconstruct a 3D model. It is shown that a model with sub-centimeter accuracy can be obtained after noise removal. However, noise removal also reduces the point cloud density, making the 3D point cloud unsuitable for quantification of small-scale damages such as fine cracks. Therefore, the captured images are also analyzed using deep convolutional neural network (CNN) models to enable crack detection and segmentation. For this aim, in the second approach, the 3D model is generated by the output of CNN models to enable crack localization and quantification on 3D digital model. These two approaches were evaluated in separate case studies, showing that the proposed technique could be a valuable tool to assist human inspectors in detecting, localizing, and quantifying defects on concrete structures.
Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Bridge inspection, Computer vision, Photogrammetry, Damage detection, Damage segmentation, UAV
National Category
Infrastructure Engineering
Research subject
Structural Engineering
Identifiers
urn:nbn:se:ltu:diva-95443 (URN)10.1007/s13349-023-00680-x (DOI)000921813700002 ()2-s2.0-85146975670 (Scopus ID)
Funder
Swedish Research Council Formas, 2019-01515EU, Horizon 2020, 101012456 IN2TRACK3
Note
Validerad;2024;Nivå 2;2024-04-02 (hanlid);
Full text license: CC BY 4.0
2023-01-312023-01-312024-11-20Bibliographically approved