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

TL;DR

This paper reviews UAV-based methods for infrastructure inspection in the AEC+FM sector, highlighting recent innovations, challenges, and proposing a comprehensive multimodal data fusion framework with a case study.

Contribution

It introduces a novel workflow framework integrating multimodal sensing and transformer architectures to enhance defect detection accuracy.

Findings

UAVs effectively detect structural defects and anomalies.

The proposed framework improves detection accuracy through multimodal data fusion.

Challenges in real-time processing and data fusion remain significant.

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.