With the development of drone and image recognition technologies, using drones to capture images for engineering structural damage detection can replace inefficient and unsafe manual maintenance inspections. This paper focuses on the pairing method between drone devices and the BIM components of large buildings, with Revit’s secondary development serving as the technical approach. A plugin for pairing drone images with BIM components is developed. The research first establishes the technical scheme for pairing drone images with BIM models. Then, the positional and directional information of the drone images are extracted, and a reference coordinate system for the drone’s position and image capture orientation is introduced. The transformation method and path from the real-world coordinate system to the Revit 2023 software coordinate system are explored. To validate the interactive logic of the transformation path, a pairing plugin is developed in Revit. By employing coordinate conversion and Revit family loading procedures, the relative position and capture orientation of the drone are visualized in the 3D BIM model. The plugin uses techniques such as family element filtering and ray tracing to automatically identify and verify BIM components, ensuring the precise matching of drone images and BIM components. Finally, the plugin’s functionality is verified using a high-rise building in Wuhan as a case study. The results demonstrate that this technological approach not only improves the efficiency of pairing drone images with models in building smart maintenance but also provides a fast and reliable method for pairing drones with BIM systems in building management and operations. This contributes to the intelligent and automated development of building maintenance.


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    Title :

    Research on the Method for Pairing Drone Images with BIM Models Based on Revit


    Contributors:
    Shaojin Hao (author) / Xinghong Huang (author) / Zhen Duan (author) / Jia Hou (author) / Wei Chen (author) / Lixiong Cai (author)


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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