Mapping and modelling defect data from UAV captured images to BIM for building external wall inspection

With the increase of service years, external walls of high-rise buildings tend to suffer from a variety of defects which impose great safety risks. Traditional methods for inspecting high-rise building external walls require inspectors to work at height and identify defects manually, which is dangerous and inefficient. In recent years, there has been an increasing trend of using unmanned aerial vehicles (UAV) for inspecting building external walls, but how to manage the information obtained by the UAV is still a problem. In addition, although building information modelling (BIM) with rich geometric and semantic information has been applied in the construction engineering industry, BIM models usually lack updated condition data of facilities. Therefore, this paper presents a method for managing the inspection results of building external walls by mapping defect data from UAV images to BIM models and modelling defects as BIM objects. First, images of building external walls obtained by UAV are processed and useful information such as coordinates are extracted. Considering the small scale of single buildings, a simplified coordinate transformation approach is developed to transform location of real-world defects to coordinates in the BIM model. Meanwhile, a deep learning-based instance segmentation model is developed to detect defects in the captured images and extract their features. In the end, the identified defects are modelled as new objects with detailed information and mapped to the corresponding location of the related BIM component. To validate the feasibility, the proposed method has been applied to a real office building, which successfully mapped and integrated the defects of external walls with the BIM model. This study is applicable to both buildings and infrastructure, and is expected to facilitate structure inspection and decision making in maintenance with integrated data of as-is condition and as-built BIM.