Automatic Identification and Intelligent Optimization of Tunnel-Free Curve Reconfiguration

Abstract

Extracting complete cross-sectional geometric features from the large amount of point cloud data acquired by laser scanners plays an important part in the detection of deformations in tunnel inspection projects. Tunnel cross-sections have symmetrical geometric features, and information is traditionally collected manually. The traditional manual extraction of point clouds is inefficient and limited by the subjectivity of the operators when addressing the problems. This paper proposes a new algorithm for the automatic identification of tunnel lining section curves, the rapid separation of common interference targets, and the optimization of curve geometry features. The innovation of this approach lies in the combination of B-spline and Euclidean clustering methods and the comprehensive evaluation of the denoising results in terms of precision, recall, F-score, and rand index (RI). In this way, the automatically extracted health point cloud data are refitted to optimize the tunnel profile model.