Automatic Detection of Building Surface Structure Damage Based on Multi-UAV Collaboration

The health condition of a building is related to the safety of people's lives and properties, so it is especially important to accurately detect its surface structure damage. Initial assessment of a building's health condition often involves identifying these cracks on its surface images. However, capturing these images traditionally relies on human labor, and the small pixel size of cracks in two-dimensional images makes segmentation challenging. In this study, a pioneering approach is introduced for automatic detection of building surface structure damage based on multi-UAV collaboration. The method comprises two primary components. Firstly, we introduce a reconstruction mathematical model based on MVS reconstruction rules to estimate the reconstruction quality. This model guides the extension of multi-UAV trajectories, considering trajectory energy consumption and security, enabling automated image capture and the creation of high-quality 3D models. Secondly, we design a spatial attention mechanism that incorporates Canny edge detection information and improve the deeplabV3 model to realize the automatic recognition of small cracks on building surfaces. Real-world experiments demonstrate that our method facilitates collaborative multi-UAV image capture, supporting high-quality 3D reconstruction and achieving precise crack segmentation.