Automated Defect Inspection in Building Construction with Multi-Sensor Fusion and Deep Learning

Automated Defect Inspection in Building Construction with Multi-Sensor Fusion and Deep Learning Juhyeon Kim, Jeehoon Kim, Yulin Lian, Hyoungkwan Kim Pages 1097-1103 (2024 Proceedings of the 41st ISARC, Lille, France, ISBN 978-0-6458322-1-1, ISSN 2413-5844) Abstract: The occurrence of defects during the building construction process significantly impacts housing quality. One such defect, the distortion of a building's framework, affects both sustainability and aesthetics. This study presents an automated technique for inspecting framework distortion in building construction by measuring the angles between walls. The proposed method employs a portable data acquisition system that allows for dynamic data collection. The system's accuracy is enhanced through calibration based on terrestrial laser scanning (TLS) data as a reference. Point cloud data are registered to form a map of the interior space, leveraging a deep learning algorithm to visualize framework distortions. When tested in an apartment construction environment, the method reduces data acquisition time compared to the TLS-based approach, while maintaining precision with an average angular error of 0.28 degrees. This study demonstrates a cost-effective and accurate solution for defect inspection in the construction industry. Keywords: Defect Inspection, Mobile Data Acquisition System, Multi-Sensor Calibration, Point Cloud Registration, Defect Visualization DOI: https://doi.org/10.22260/ISARC2024/0142 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley