Rapid Inspection of Large-Span Roofs Using Un-Manned Aerial Vehicle Oblique Photography and Point Cloud Data

Traditional methods for inspecting large-span roofs face challenges such as extensive measurement areas, difficult positioning, low efficiency, and insufficient utilization of secondary analysis of inspection data. To address these issues, this study proposes a secondary analysis method based on UAV technology using oblique photography and point cloud data, aimed at rapidly detecting roof surface slope variations and predicting drainage pathways. The research employs a gimbal-equipped UAV to capture tilted images, generates point cloud data through aerial triangulation, and subsequently achieves 3D modeling and texture mapping. Based on the diagonal intersection grid of the roof surface, a point cloud feature processing algorithm is used to extract the axis, and the layout and slope of each region are calculated and compared with design drawings. The results demonstrate that this method can efficiently and accurately detect the arrangement of the roof surface grid and slope variations, providing significant guidance for the subsequent installation and drainage design of metal roofs.