Automatic Identification and Intelligent Optimization of Tunnel-Free Curve Reconfiguration

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