A new literature review and proposed framework detail the use of Unmanned Aerial Vehicles (UAVs) for infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) sector. The paper synthesizes over 150 studies, highlighting methodologies for data acquisition, defect detection, and decision support, incorporating advanced machine learning models like YOLO and Faster R-CNN. It also discusses the integration of RGB imagery, LiDAR, and thermal sensing with transformer-based architectures to enhance accuracy in identifying structural defects and anomalies. The research identifies remaining challenges in real-time processing and multimodal data fusion, while proposing future directions such as lightweight AI models and adaptive flight planning. AI
IMPACT This research could lead to more efficient and accurate infrastructure inspections through advanced AI and sensor fusion.
RANK_REASON The cluster contains an academic paper detailing a literature review and a proposed framework for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
