Recent events in both armed conflict and the civil aviation space continue to highlight the threat of Unmanned Aerial Systems (UAS), often referred to as drones. Most drone counter measure systems and all early warning systems require drone detection. A number of drone detection techniques, including radars, RF signal capture and optical sensing have been developed to provide this capability. These techniques all have different advantages and disadvantages and a robust counter UAS (C-UAS) or UAS early warning system should combine several of these systems. One of the available detection systems is computer vision using deep learning and optical sensors. Due to the rapid advancement of this area, there are many options for practitioners seeking to utilize cutting edge deep learning techniques for optical UAS detection. In this study, we provide a comparative performance analysis of four state-of-the-art deep learning-based object detection algorithms, namely YOLOv5 small and large, SSD and Faster RCNN. We show that the YOLOv5 based models and Faster RCNN model are very close to each other in terms of accuracy while they outperform SSD. The YOLOv5 based models are also significantly faster than both SSD and Faster RCNN algorithms. Our analysis suggests that among the investigated algorithms, YOLOv5 small provides the best trade-off between accuracy and speed for a C-UAS self-protection or early warning system.

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