VISION-BASED APPROACHES FOR QUANTIFYING CRACKS IN CONCRETE STRUCTURES

In this paper, a combination of photogrammetric, computer-vision, and deep-learning approaches are proposed for accurate detection and quantification of cracks from the images of concrete structures. In particular, a semantic segmentation approach using UNet is applied, which is trained on a customi...

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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIII-B2-2020; pp. 1167 - 1174
Main Authors Shokri, P., Shahbazi, M., Lichti, D., Nielsen, J.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 14.08.2020
Copernicus Publications
Subjects
Calibration
Cameras
Concrete structures
Cracks
Detection
Flaw detection
Image reconstruction
Image segmentation
Orientation
Parameters
Photogrammetry
Pixels
Vision
Workflow
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Summary:In this paper, a combination of photogrammetric, computer-vision, and deep-learning approaches are proposed for accurate detection and quantification of cracks from the images of concrete structures. In particular, a semantic segmentation approach using UNet is applied, which is trained on a customized dataset of real-world images. Then, two photogrammetric methods are assessed for reconstructing the full figure of the cracks from stereo images. One approach is based on detecting the dominant structural plane surrounding the crack and projecting the crack pixels to this 3D plane. The second approach is based on matching the crack pixels across two images. To be able to perform the 3D reconstructions accurately, a rigorous calibration of the intrinsic calibration parameters of the cameras is performed. The relative orientation parameters between the stereo cameras are also determined in the calibration procedure. Extensive experiments are performed to evaluate each phase of this detection-and-quantification workflow. In general, cracks can be detected with an average precision of 87.48% and recall of 87.45%. They can be reconstructed in 3D with an accuracy as high as 0.05 mm.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLIII-B2-2020-1167-2020