The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1107–1114, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1107-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1107–1114, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1107-2020

  14 Aug 2020

14 Aug 2020

SENSOR EVALUATION FOR CRACK DETECTION IN CONCRETE BRIDGES

D. Merkle1,2, A. Schmitt1,2, and A. Reiterer1,2 D. Merkle et al.
  • 1Department of Sustainable Systems Engineering INATECH, University of Freiburg, 79110 Freiburg, Germany
  • 2Fraunhofer Institute for Physical Measurement Techniques IPM, 79110 Freiburg, Germany

Keywords: crack detection, concrete bridges, camera system, laser scanning, laser triangulation, machine learning

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.