Traditional maritime surveillance often relies on static cameras, limiting coverage and efficiency in detecting vessels. This paper proposes a novel real-time UAV-based vessel detection system that addresses these limitations. The system leverages a Dynamic Camera Control Strategy that overcomes fixed field-of-view constraints by adjusting camera angles based on predefined search patterns, historical data, and real-time sensor feedback. This systematic scanning approach ensures comprehensive coverage, minimizing missed detections. Furthermore, a Feature-Based Prioritization Scheme facilitates real-time target confirmation by analyzing features like size, shape, and, if applicable from additional sensors, other relevant data. This scheme prioritizes promising candidates for further analysis, reducing false positives. By comparing features with a reference vessel stored in the system (using a ResNet50-based module), the system effectively discriminates between vessels and other objects, while movement analysis helps distinguish stationary objects. Our system was evaluated using real-world testing and simulation, demonstrating significant improvements in detection accuracy and processing speed compared to state-of-the-art methods. This research presents a valuable contribution to UAV-based maritime surveillance, enhancing operational efficiency and detection accuracy for various real-world applications.