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Sensor evaluation for crack detection in concrete bridges

 
: Merkle, Dominik; Schmitt, Annette; Reiterer, Alexander

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Volltext ()

Paparoditis, Nicolas (Hrsg.) ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
XXIV ISPRS Congress, Commission II : Virtual Event, 31 August - 2 September 2020, Nice (France)
Istanbul: ISPRS, 2020 (ISPRS Archives XLIII-B2-2020)
S.1107-1114
International Society for Photogrammetry and Remote Sensing (ISPRS Congress) <24, 2020, Online>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPM ()
crack detection; laser scanning; camera system; laser triangulation; machine learning; concrete bridges

Abstract
Bridges are one of the most critical traffic infrastructure objects, therefore it is necessary to monitor them at regular intervals. Nowadays, this monitoring is made manually by visual inspection. In recent projects, the authors are developing automated crack detection systems to support the inspector. In this pre-study, different sensors, like different camera systems for photogrammetry, a laser scanner, and a laser triangulation system are evaluated for crack detection based on a defined required minimum crack width of 0.2 mm. The used test object is a blasted concrete plate, sized 70 cm × 70 cm × 5 cm and placed in an outdoor environment. The results of the data acquisition with the different sensors are point clouds, which make the results comparable. The point cloud from the chosen laser scanner is not sufficient for the required crack width even at a low speed of 1 m/s. The RGB or intensity information of the photogrammetric point clouds, even based on a low-cost smartphone camera, contain the targeted cracks. The authors advise against using only the 3D information of the photogrammetric point clouds for crack detection due to noise. The laser triangulation system delivers the best results in both intensity and 3D information. The low weight of camera systems makes photogrammetry to the preferred method for an unmanned aerial vehicle (UAV). In the future, the authors aim for crack detection based on the 2D images, automated by using machine learning, and crack localisation by using structure from motion (SfM) or a positioning system.

: http://publica.fraunhofer.de/dokumente/N-599626.html