Low in Resolution, High in Precision: UAV Detection with Super-Resolution and Motion Information Extraction

Abstract

The rapid development of unmanned aerial vehicle (UAV) market presents potential threats to public security and personal privacy, and the vision sensors are widely deployed to detect the invasive UAVs because of the intuitivity and accessibility of the video. However, the small pixel area and weak morphological characteristics of distant invasive UAVs pose a considerable challenge to the detection precision. Prior work on UAV detection simply focuses on the information fusion between different feature layers, but ignoring the feature information inside each layer. In addition, to detect small UAV in video streams, the motion information of the target is also a noteworthy feature. In this regard, we propose a feature super-resolution-based UAV detector with motion information extractor. The proposed network fully utilizes the motion information of UAVs between temporal frames and the spatial invariant features between different resolution frames to pursue a high-accuracy small UAV detection performance. Experiments on Drone vs Birds dataset are carried out, and it is demonstrated that a higher detection accuracy on small UAVs is achieved compared with the baseline.