Building facade inspection is essential for maintaining structural safety, yet conventional approaches remain limited by high-altitude risks, labour intensity, and inconsistent evaluation. This study develops Zhiyi Building Inspection, an integrated unmanned aerial vehicle-artificial intelligence-building information modeling (UAV-AI-BIM) framework for automated facade damage detection. The system combines UAV-based image acquisition with real-time kinematic (RTK) positioning, an improved You Only Look Once version 5 Large (YOLOv5l) detection model incorporating Complete Intersection over Union (CIoU) loss and Convolutional Block Attention Module (CBAM), Sobel-based defect quantification, severity assessment, and BIM-assisted visualisation. A dataset of 3,982 high-resolution images covering eight facade-defect categories was constructed for model training and evaluation. Experimental results demonstrate that the proposed framework improves detection accuracy while maintaining real-time processing capability and provides engineering-oriented outputs including defect location, severity, and inspection reports. The study demonstrates the feasibility of integrating UAV sensing, artificial intelligence, and digital building management for safer and more efficient facade maintenance.
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Share and Cite
Tang, W., Zhang, H., Li, M., Yang, S., Liu, F. (2026) Zhiyi Building Inspection: A UAV-based Building Facade Damage Detection System. Scientific Research Bulletin, 3(4), 46-61. https://doi.org/10.71052/srb2024/MTPY6488