Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain. By synthesizing insights from over 150 studies, this review paper highlights UAV-based methodologies for data acquisition, photogrammetric modeling, defect detection, and decision-making support. Key innovations include path optimization, thermal integration, and advanced machine learning (ML) models such as YOLO and Faster R-CNN for anomaly detection. UAVs have demonstrated value in structural health monitoring (SHM), disaster response, urban infrastructure management, energy efficiency evaluations, and cultural heritage preservation. Despite these advancements, challenges in real-time processing, multimodal data fusion, and generalizability remain. A proposed workflow framework, informed by literature and a case study, integrates RGB imagery, LiDAR, and thermal sensing with transformer-based architectures to improve accuracy and reliability in detecting structural defects, thermal anomalies, and geometric inconsistencies. The proposed framework ensures precise and actionable insights by fusing multimodal data and dynamically adapting path planning for complex environments, presented as a comprehensive step-by-step guide to address these challenges effectively. This paper concludes with future research directions emphasizing lightweight AI models, adaptive flight planning, synthetic datasets, and richer modality fusion to streamline modern infrastructure inspections.

翻译：无人机正在深刻变革建筑、工程、施工与设施管理领域的基础设施检测方式。本文通过综合150余项研究成果，系统综述了基于无人机的数据采集、摄影测量建模、缺陷检测与决策支持方法。关键技术进展包括路径优化、热成像集成以及采用YOLO、Faster R-CNN等先进机器学习模型的异常检测技术。无人机已在结构健康监测、灾害应急响应、城市基础设施管理、能效评估及文化遗产保护等方面展现出重要价值。尽管取得显著进展，实时处理、多模态数据融合与模型泛化能力仍面临挑战。本文基于文献综述与案例研究，提出集成RGB影像、激光雷达与热传感数据的工作流框架，结合Transformer架构提升结构缺陷、热异常及几何不一致性检测的精度与可靠性。该框架通过融合多模态数据并动态适应复杂环境下的路径规划，确保生成精确且可操作的洞察，并以逐步实施指南的形式系统应对现有挑战。最后，本文展望了未来研究方向，强调轻量化AI模型、自适应飞行规划、合成数据集及深度融合多模态数据在提升现代基础设施检测效能方面的重要性。