Deep learning-based 3D digital damage map of vertical-type tunnels using unmanned fusion data scanning

• Published in: Automation in construction 2024-06, Vol.162, p.105397, Article 105397
• Main Authors: Jang, Keunyoung, Park, Sinzeon, Jung, Hyunjun, Yoo, Hoon, An, Yun-Kyu
• Format: Article
• Language: English
• Subjects: 3D digital damage map; 3D modeling; Automated damage evaluation; Damage mapping; Deep learning; Optimal data selection; Unmanned fusion data-scanning system; Vertical-type tunnel
• ISSN: 0926-5805; 1872-7891
• DOI: 10.1016/j.autcon.2024.105397

Expert-dependent visual inspection of vertical-type tunnels is often not only dangerous for workers, but also unreliable of the inspection results. To address the technical issues, a deep learning-based 3D digital damage mapping technique using an unmanned fusion data-scanning system is described in this article. The proposed system acquires raw digital images and point cloud data by moving vertically within the tunnel. Subsequently, optimal images are automatically selected and used for deep learning-based damage evaluation and fixed camera pose-based 3D digital modeling. Finally, a 3D digital damage map is generated by mapping the damage features onto the 3D digital map based on the predetermined camera pose. The proposed technique was experimentally validated in two different in-situ testbeds in South Korea, revealing that averaged precision and recall values are 90.89% and 98.18%, respectively. It is expected that the proposed technique can improve in-situ workability as well as data reliability. •Unmanned fusion data-scanning system is developed for vertical-type tunnel inspection.•Deep semantic segmentation network is developed for automated detection of multi-type damage.•Fusion data-based damage quantification algorithm is developed.•Fixed camera pose-based modeling and damage mapping algorithms are newly proposed for 3D digital damage map generation.•The proposed technique is experimentally validated in in-situ vertical-type tunnels.