Domain knowledge-enhanced region growing framework for semantic segmentation of bridge point clouds

Abstract

Utilising domain knowledge (DK) to semantically segment bridge point clouds has attracted growing research interest. However, current approaches are often tailored to specific bridges, limiting their general applicability. To address this problem, this paper introduces a DK-enhanced Region Growing (DKRG) framework for point cloud semantic segmentation of reinforced concrete (RC) girder bridges. Inspired by the vertical layout characteristics of bridges, the generation of DK-based point features from Finite Element Analysis (FEA) is first proposed. Then, DKRG is employed to segment bridge components from substructures to superstructures by leveraging an “easy-to-difficult” strategy. Validation results demonstrate the effectiveness of our method, achieving the lowest mean Intersection over Union (mIoU) of 95.47% for the entire bridge and 93.44% for different component types. This study provides a new DK-based framework for semantic segmentation of RC girder bridges and sheds new light on using FEA-generated point features.