| INTELLIGENT 3D CRACK RECONSTRUCTION USING CLOSE RANGE PHOTOGRAMMETRY IMAGERY |
| Paper ID : 1185-GEOSPATIAL (R4) |
| Authors: |
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Soheil Majidi * Faculty of Surveying and Geospatial Engineering / University of Tehran / Tehran / Iran |
| Abstract: |
| Civil infrastructures structural health monitoring (SHM) and their preservation from deterioration is a crucial task. In general, natural disasters like severe earthquake, extreme landslide, subsidence or intensive flood directly influence on the health of the civil structures such as buildings, bridges, roads, and dams. Evaluation and inspection of defects and damages of the aforementioned structures help to preserve them from destruction by accelerating the rehabilitation and reconstruction. Large numbers of the structures increase the necessity for an automatic, precise, and periodic assessment and inspection at an early stage which may appear as cracks. For this purpose, in this study, two-step procedure is proposed which consists of crack segmentation and its 3D reconstruction. The crack segmentation is carried out by using Deeplabv3+ architecture and Xception as backbone. Next, Squeeze-and-Excitation are added as an attention module to increase the accuracy. The integration of predicted masks and original images to a structure from motion procedure are additionally taken into account. In the last step, Ground Control Points and scale bars are considered to overcome the problem of datum rank deficiency in absolute orientation through bundle adjustment procedure in aerial triangulation. The most probable segmented cracks are overlaid on the 3D point clouds in the global coordinate system with true scale. Our network is trained based on 8000 images and their corresponding masks which lead to 69% in Intersection over Union (IoU) index. The analyses demonstrate the accuracy of crack reconstruction at the level of lower than one millimetre which is validated with a scale bar. |
| Keywords: |
| Crack Segmentation, Crack 3D Reconstruction, DeepLabv3+, Xception, Structural Health Monitoring, Deep Learning, Structure From Motion, Civil Infrastructures |
| Status : Paper Accepted (Poster Presentation) |
