Current inspections of exterior wall insulation layers in high-rise buildings face challenges such as high risk, low efficiency, and difficulty in scaling up manual inspections, driving the integration of unmanned aerial vehicle (UAV) remote sensing and lightweight depth vision recognition technologies in building inspection. To achieve automatic identification and robust tracking of key defects such as insulation layer cracks, delamination, and moisture seepage, this paper proposes an automatic defect identification method for exterior wall insulation layers based on UAV image recognition technology. This method first acquires multi-source images using a UAV, then employs a lightweight exterior wall defect detection network model for accurate defect identification, and finally introduces a post-processing extension framework with spatiotemporal consistency and structural region constraints to achieve dual localization of two-dimensional defects based on structural attribution and three-dimensional coordinates. Results show that this method has low latency (Formula: see text Formula: see textms) and low GPU usage (Formula: see text%). In on-site inspection tests at a real building height of 60 m, the method achieves an engineering detection rate of 0.936. This demonstrates that the method can accurately identify exterior wall defects with low resource consumption, making it suitable for long-term, high-frequency, and low-consumption deployment of UAVs for edge-based inspections. The proposed method provides a low-altitude remote sensing-based intelligent inspection framework for UAV-based urban building facade monitoring, which can serve as a reference for engineering applications such as periodic inspection and large-scale screening.
Xiaojing Yu (Fri,) studied this question.