Work

Computer vision framework for UAV-based defect inspection of existing buildings with cement-mortar facades and BIM integration

Year: 2026

Type: article

Abstract: Defect recognition of existing buildings with cement-mortar facades remains challenging due to diverse defect types, inefficient and risky manual inspection, and missing BIM models. This paper presents an automated framework that combines UAV imagery and computer vision techniques for defect recognition and BIM integration. The framework employs RTK-guided UAV flight for image acquisition, OneFormer3D with DBSCAN for envelope segmentation, YOLOv9 for window detection, Mask2Former with SAHI for defect segmentation, and Revit/Dynamo for automated BIM generation. Validation on a 16-story building achieved 96.97% mAP@50 for envelope segmentation, 92.4% [email protected] for window detection, and 65.12% AP50 for defect segmentation, with defects mapped to a queryable BIM model. This framework reduces manual labor and high-altitude risk, providing a digital O&M foundation for existing buildings without BIM. Future work should improve small-crack accuracy, extend the method to other facade materials, and automate flight path planning.

Source: Automation in Construction

DOI: 10.1016/j.autcon.2026.107316

Authors: Zeyu Li, Qian Wang, Hong Huang, Xiang Nie, Yehao Wang

Institution: Southeast University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $5,490