Abstract
We present a novel geometry-based approach for the detection of small-scale cracks in a temporal series of 3D-reconstructions of concrete objects such as pillars and beams of bridges and other infrastructure. The detection algorithm relies on a geometry-derived coloration of the 3D surfaces for computing the optical flow between time steps. Our filtering technique identifies cracks based on motion discontinuities in the local crack neighborhood. This approach avoids using the material color which is likely to change over time due to weathering and other environmental influences. In addition, we detect and exclude regions with significant local changes in geometry over time e.g. due to vegetation. We verified our method with reconstructions of a horizontal concrete beam under increasing vertical load at the center. For this case, where the main crack direction is known and a precise registration of the beam geometries over time exists, this approach produces accurate crack detection regardless of substantial color variations and is also able to mask out regions with simulated growth of vegetation over time.
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Acknowledgments
This work was supported by the German Federal Ministry of Education and Research (BMBF) under the project number 13N14657 (Project AISTEC). The concrete beam data set used in this work is courtesy of Bauhaus-Universität Weimar.
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Matthes, C., Kreskowski, A., Froehlich, B. (2019). A Geometry-Based Method for the Spatio-Temporal Detection of Cracks in 4D-Reconstructions. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_47
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