Semantic segmentation and sensor fusion in point cloud processing for estimation of bridge structural parameters

Abstract

In this study, we implemented sensor fusion by combining point cloud data and image data obtained from a terrestrial laser scanner, and mapped the segmentation results from the images onto the point clouds. Semantic segmentation of the images was performed using DeepLabv3+ to classify into wheel guard and background. Also, the edge information was updated using the Segment Anything Model, then segmentation information was stored in the point clouds using the camera’s external and internal parameters. Utilizing this information enabled the measurement of bridge widths. By leveraging the detailed information from the image data and the three-dimensional information from the point cloud data, we were able to achieve an analysis that extract detail and structural information, while also efficiently processing large files of point cloud data.