Seite - 782 - in Book of Full Papers - Symposium Hydro Engineering

[8] RABAH M., ELHATTAB A., FAYAD A. Automatic concrete cracks detection and mapping of terrestrial laser scan data. National Research Institute of Astronomy and Geophysics. 2(2). pp. 250–255, 2013. [9] MOHAN A., POOBAL S. Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal. 12p, 2017. [10] VALENÇA J., DIAS-DA-COSTA D., JÚLIO E., ARAÚJO H., COSTA H. Automatic crack monitoring using photogrammetry and image processing. Measurement. 46(1). pp. 433–441, 2013. [11] KALENJUK S., LIENHART W. Automated Surface Documentation of Large Water Dams Using Image and Scan Data of Modern Total Stations. FIG Working Week 2017, 8p, 2017. [12] LU D., MAUSEL P., BRONDIZIO E.S., MORAN E. Change Detection Techniques. International Journal of Remote Sensing. 25(12). pp. 2365– 2407, 2004. [13] NIEMEYER I., MARPU P.R., NUSSBAUM S. Change detection using the object features. IEEE International Geoscience and Remote Sensing Symposium. pp. 2374–2377, 2007. SUMMARY In this paper, we presented a new approach for surface documentation using scan and image data of modern total stations. The proposed method is easy to perform for surveyors and requires less effort in data acquisition and processing compared to other measurement principles (e.g. documentation with UAVs). The used total stations benefit from classic setup routines providing referenced point clouds and oriented images at the time of measurement. Hence, if stable points with known coordinates exist for referencing the measurements, the surveyor has to perform the time-consuming scanning process only once. It is thus possible to combine geometry acquired at the initial measurement epoch with photos captured at different measurement times to derive textured 3D models and ortho images. We introduced a novel surface monitoring solution to address the risks related to visual changes on dam surfaces. By applying image-processing techniques to the acquired overview images, we automatically detect concrete deficiencies. With a database containing information from previous surveys, the system identifies newly emerged defects directly on-site. Subsequently, the instrument takes telescope images and scans dense point clouds of selective defects. As a result, the developed system provides multi temporal ortho images with high resolution, which we utilize to detect and to quantify changes on dam surfaces. Our proposed approach delivers data, which is accurate, complete and objective. However, our system does not intend to replace the human inspector but it aims to provide a valuable data basis to enhance the safety assessment of large concrete dams. 782