Parallel Dual-Level Attention-Enhanced Semantic Segmentation of 3D Point Clouds in Tunnel Construction
TL;DR
Validated on real tunnel data, PDAPointNet++ outperforms models such as PointNet, PointNet++, PointMLP, and dynamic graph convolutional neural network (DGCNN) and the impact of different WDFL balancing coefficients on model performance is discussed.
Abstract
Drill-and-blast is the primary method used for constructing mountain tunnels in China. The tunnel face area contains rich geological and structural information, necessitating high-precision semantic segmentation to support automated measurement during the construction process. This paper proposes PDAPointNet++, a novel network for semantic segmentation of point clouds in tunnel construction scenarios. The method introduces three key innovations: (1) a parallel dual-level attention module (PDAM) to enhance spatial and channel feature focus; (2) integration of PDAM into the set abstraction and feature propagation layers; and (3) a weighted dice focal loss (WDFL) to address data imbalance. Validated on real tunnel data, PDAPointNet++ outperforms models such as PointNet, PointNet++, PointMLP, and dynamic graph convolutional neural network (DGCNN). From the perspective of practical engineering applications, the accuracy of the segmentation results is further verified through rock mass discontinuity measurement on the tunnel face, and the impact of different WDFL balancing coefficients on model performance is discussed.
