Semantic Segmentation of Heavy Construction Equipment Based on Point Cloud Data

Abstract

Most of the currently developed 3D point cloud data-based object recognition algorithms have been designed for small indoor objects, posing challenges when applied to large-scale 3D point cloud data in outdoor construction sites. To address this issue, this research selected four high-performance deep learning-based semantic segmentation algorithms for large-scale 3D point cloud data: Rand-LA-Net, KPConv Rigid, KPConv Deformable, and SCF-Net. These algorithms were trained and validated using 3D digital maps of earthwork sites to build semantic segmentation models, and their performance was tested and evaluated. The results of this research represent the first application of 3D semantic segmentation algorithms to large-scale 3D digital maps of earthwork sites. It was experimentally confirmed that object recognition technology can be implemented in the construction industry using 3D digital maps composed of large-scale 3D point cloud data.