UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM

2026-01-16

AI Summary

This study addresses the limitations of traditional inspection methods in the architecture, engineering, construction, and facility management (AEC+FM) domain—namely, low efficiency, incomplete coverage, and poor real-time performance—by proposing a multimodal unmanned aerial vehicle (UAV) inspection framework that integrates RGB, LiDAR, and thermal imaging. The framework combines dynamic path planning with a Transformer-based multimodal fusion architecture and incorporates state-of-the-art detection models such as YOLO and Faster R-CNN to achieve high-precision identification of structural defects, thermal anomalies, and geometric deviations. By innovatively integrating thermal imaging with optimized flight paths, the approach significantly enhances detection accuracy and adaptability in complex environments. Experimental validation in scenarios including structural health monitoring, disaster response, and urban infrastructure management demonstrates the framework’s effectiveness, substantially improving both inspection precision and intelligent decision-making support.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• UAV
• infrastructure inspection
• multimodal data fusion
• real-time processing
• generalizability

Innovation

• multimodal data fusion
• transformer-based architecture
• adaptive path planning
• UAV-based inspection
• structural defect detection