Although a scaffold is an essential structure in the construction industry, it may also be a dangerous factor that causes fatalities. However, the process of monitoring the scaffold is labor-intensive because it is conducted by the subjective observation of safety managers. To address this issue, we propose an automatic scaffold 3D reconstruction method using 3D point cloud data acquired using a robot dog. The method consists of three steps: 1) data acquisition of scaffold point clouds through a robot dog scanning system, 2) deep learning-based 3D semantic segmentation, and 3) automatic formation of a 3D CAD model. We created 15 robot dog datasets for training the segmentation model. The proposed method was tested at a different site where a scaffold with a representative structure was attached to the wall. The proposed method demonstrated an excellent performance of point cloud segmentation with a 90.84% F1 score.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (No. 2021R1A2C2004308 ) and the National R&D Project for Smart Construction Technology (No. 21SMIP-A158708-02 ) funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport , and managed by the Korea Expressway Corporation .
© 2021 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction