Defensive UAV Detection Paper

This paper presents a high-level, ethically framed study of an aerial object detection system intended for defensive applications. We describe algorithmic approaches combining computer vision, sensor fusion, and machine learning to detect and classify small unmanned aerial vehicles (UAVs) in cluttered environments. Emphasis is placed on methodological design, evaluation metrics, dataset curation, and responsible deployment considerations. Operational details that could enable misuse (e.g., tactical deployment parameters or countermeasure instructions) are consciously omitted. We provide experimental evaluation on public and simulated datasets, discuss limitations, and outline future research directions oriented toward safety and civil compliance.