Aerial Imagery‐Based Deep Learning Framework for Vessel Detection and Maritime Boundary Surveillance

ABSTRACT Monitoring vessel activity within Exclusive Economic Zones (EEZs) is essential for maritime security, environmental protection, and sustainable resource management. This study presents a novel framework that combines high‐resolution aerial imagery, photogrammetric geolocation techniques, and deep learning–based object detection to detect and monitor vessels near maritime boundaries. Using the SeaDronesSee dataset, vessels are automatically detected with YOLOv8 and georeferenced through image metadata, enabling accurate transformation from image to geographic coordinates. Spatial queries with EEZ boundary datasets are then applied to identify potential violations. Experimental evaluation demonstrates a detection accuracy of 98.3%, with robust performance across varied vessel types and imaging conditions. The framework is further supported by a lightweight security layer to ensure reliable transmission of boundary violation alerts. This integration of photogrammetric image analysis, automated object detection, and geospatial boundary validation provides an efficient and scalable approach to maritime monitoring, contributing to the advancement of remote sensing and photogrammetric applications in marine surveillance.