Abstract

Unmanned Aerial Vehicles (UAVs) have experienced remarkable progress and widespread utilization, highlighting the need for robust detection and classification systems to ensure safety and security. This paper presents a comprehensive study on UAV and payload detection and classification, emphasizing the fusion of multiple data sources to enhance accuracy and reliability. A fusion system integrating radar and Pan-Tilt-Zoom (PTZ) camera data is developed and evaluated. Experimental results demonstrate the effectiveness of the proposed approach, achieving a classification accuracy of > 94% for UAV detection and > 90% for payload classification. The findings underscore the system’s potential for real-world applications in UAV and payload detection and classification scenarios, addressing the growing demands in this field.