BIM ‐Integrated UAV ‐Based Defect Detection With Edge Computing for Infrastructure Inspection

This paper presents a novel approach for defect detection in infrastructure using unmanned aerial vehicles (UAVs) integrated with Building Information Modeling (BIM) and edge computing. The proposed system leverages BIM's rich geometric and semantic data to guide UAV flight paths and defect localization, while edge computing is employed to perform real‐time defect detection onboard the UAV. The methodology includes four key components: BIM‐driven UAV flight planning, lightweight onboard defect detection via edge‐enabled deep learning models, coordinate transformation for BIM alignment, and the instantiation of detected defects into the BIM model for lifecycle management. Experimental results demonstrate that the proposed BIM‐UAV‐Edge framework outperforms traditional image processing and deep learning‐based approaches in terms of accuracy, latency, and system efficiency. With an F1‐score of 0.92 compared to 0.87, a processing latency of 110 ms versus 300 ms, and network bandwidth efficiency of 4 Mbps against 30 Mbps, this integrated solution significantly reduces data transmission and improves real‐time feedback, offering a robust framework for large‐scale infrastructure monitoring and predictive maintenance.