Automated recognition and rebar dimensional assessment of prefabricated bridge components from low-cost 3D laser scanner

Abstract

Dimension quality inspections for rebar spacing and length of prefabricated bridge components are critical for subsequent efficient assembly on-site. Currently, the traditional manual inspection method is time-consuming, labor-intensive, and error-prone. The existing commercial 3D LiDAR-based methods are difficult in making balances between inspection cost, efficiency, and accuracy. Thus, the automated recognition and segmentation method of 3D point clouds acquired by a self-developed low-cost LiDAR is proposed to evaluate the rebar spacing and length of bridge prefabricated components. Due to the high-cost commercial 3D laser scanner, a spinning 2D LiDAR-based low-cost 3D laser scanner device and geometric calibration model were developed to acquire the 3D spatial point cloud. Then, two-stage automated point cloud segmentation framework is proposed with coarse extraction of the point cloud in the interest region and fine recognition of point cloud using machine learning methods, including plane segmentation and circle fitting employing random sample consensus (RANSAC) algorithm, and rebars segmentation utilizing Gaussian mixture model (GMM) and density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed system was evaluated in two prefabricated concrete columns and shows the potential for improving the quality inspection efficiency and accuracy.