This systematic review explores the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) in automating road signage inventory creation, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to analyze recent advancements. The study evaluates cutting-edge technologies, including UAVs equipped with deep learning algorithms and advanced sensors like light detection and ranging (LiDAR) and multispectral cameras, highlighting their roles in enhancing traffic sign detection and classification. Key challenges include detecting minor or partially obscured signs and adapting to diverse environmental conditions. The findings reveal significant progress in automation, with notable improvements in accuracy, efficiency, and real-time processing capabilities. However, limitations such as computational demands and environmental variability persist. By providing a comprehensive synthesis of current methodologies and performance metrics, this review establishes a robust foundation for future research to advance automated road infrastructure management to improve safety and operational efficiency in urban and rural settings.
PRISMA Review: Drones and AI in Inventory Creation of Signage
2025
Article (Journal)
Electronic Resource
Unknown
LiDAR , UAVs , Yolo , R-CNN , AI , data fusion , Motor vehicles. Aeronautics. Astronautics , TL1-4050
Metadata by DOAJ is licensed under CC BY-SA 1.0
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