Enhanced Detection of Small Unmanned Aerial System Using Noise Suppression Super-Resolution Detector for Effective Airspace Surveillance

초록

Small unmanned aerial systems have become increasingly prevalent in various fields, including agriculture, logistics and the public sector, but concerns over misuse, such as military intrusions and terrorist attacks, highlight the necessity for effective aerial surveillance. Although conventional radar systems can detect large areas, they face challenges in accurately identifying small drones. In contrast, vision sensors offer high-resolution identification but encounter challenges in long-range detection and real-time processing. To address these limitations, this study proposes a vision sensor-based detection framework, termed the noise suppression super-resolution detector (NSSRD). To ensure the reliability and real-time capability of small drone detection, NSSRD integrates image segmentation, noise suppression, super-resolution transformation, and efficient detection processes. NSSRD divides the surveillance area into uniform sections, applies a bilateral filter to suppress noise before passing the images to an object detection model, and uses a region of interest selection process to reduce the detection area and computational load. The experimental results demonstrate that NSSRD outperforms existing models, achieving a 24% improvement in the true positive rate and a 25% increase in recall at an altitude of 40 m, validating its superior performance.

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