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In this paper, we demonstrate and evaluate a method to perform real-time object detection on-board a UAV using the state of the art YOLOv2 object detection algorithm running on an NVIDIA Jetson TX2, an GPU platform targeted at power constrained mobile applications that use neural networks under the hood. This, as a result of comparing several cutting edge object detection algorithms. Multiple evaluations we present provide insights that help choose the optimal object detection configuration given certain frame rate and detection accuracy requirements. We propose how this setup running on-board a UAV can be used to process a video feed during emergencies in real-time, and feed a decision support warning system using the generated detections.
Tijtgat et al. (Sun,) studied this question.