Target detection Bridge Modelling using Point Cloud Segmentation Obtained from Photogrameric UAV

In recent years, great efforts have been made to generate 3D models of urban structures in photogrammetry and remote sensing. 3D reconstruction of the bridge, as one of the most important urban structures in transportation systems, has been neglected because of its geometric and structural complexity. Due to the UAV technology development in spatial data acquisition, in this study, the point clouds generated from UAV-based images are used for 3D modeling of the four main elements of a bridge structure, including the railing, body, base and abutment elements. For this, a knowledge-based algorithm is proposed to provide 3D models of different types of bridge structures in GIS-based data format using the knowledge in the shape, structure and geometric relationships between the bridge’s elements. First, the fuzzy c-means clustering method including height and spectral values as well as point-based features such as the 3D density, normal vectors ​​and planarity is used to segment the point cloud. Next, a projection-based reconstruction technique, which is developed based on the geometric and structural features of each bridge element, is proposed to generate a 3D model for that element. The proposed reconstruction workflow includes the projection of point clouds to a 2D space, fitting the primitive geometric models to 2D points, locating the primitive coordinates of the models in the 2D space, and then developing 2D models into 3D space. To evaluate the proposed method, the dimensions of the structural elements in the bridge design plans are compared with the dimensions of the elements in the generated 3D model. Despite the many challenges in modeling steps, the results of this study indicate a high accuracy and ability for the proposed algorithm in 3D modeling of bridges with different geometry and designs, with a mean error and a standard deviation of about 3 cm and 1cm, respectively.