A Multimodal Artificial Intelligence-Based UAV Patrol System for Intelligent Field Monitoring and Decision Support

Abstract

Unmanned aerial vehicles (UAVs) have been widely applied in field inspection, environmental monitoring, infrastructure management, and industrial patrol tasks due to their high mobility, flexible deployment, and capability to collect large-scale aerial imagery. However, conventional UAV patrol systems mainly rely on manual image interpretation or single-modal computer vision models, which limits their ability to understand complex field conditions, integrate heterogeneous information, and support intelligent decision-making. To address these challenges, this study proposes a multimodal artificial intelligence-based UAV patrol system that integrates aerial image acquisition, visual understanding, semantic analysis, and intelligent decision support. The proposed system combines UAV-based remote sensing, visual language models, object detection, image retrieval, and knowledge-driven reasoning to transform patrol images into structured and interpretable information. Through multimodal analysis, the system can identify field objects, detect abnormal conditions, understand spatial relationships, and provide natural-language responses for field management queries. The proposed framework is designed to support applications such as industrial site inspection, infrastructure monitoring, green energy field assessment, and smart campus patrol. Experimental evaluation demonstrates that the system can effectively improve patrol efficiency, reduce manual inspection workload, and enhance the interpretability of UAV-based monitoring results. The results indicate that multimodal artificial intelligence provides a promising approach for developing next-generation UAV patrol systems with visual perception, semantic understanding, and decision-support capabilities.