Embedded Real-Time Object Detection for a UAV Warning System
Abstract
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.
Cite
Text
Tijtgat et al. "Embedded Real-Time Object Detection for a UAV Warning System." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.247Markdown
[Tijtgat et al. "Embedded Real-Time Object Detection for a UAV Warning System." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/tijtgat2017iccvw-embedded/) doi:10.1109/ICCVW.2017.247BibTeX
@inproceedings{tijtgat2017iccvw-embedded,
title = {{Embedded Real-Time Object Detection for a UAV Warning System}},
author = {Tijtgat, Nils and Van Ranst, Wiebe and Volckaert, Bruno and Goedemé, Toon and De Turck, Filip},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {2110-2118},
doi = {10.1109/ICCVW.2017.247},
url = {https://mlanthology.org/iccvw/2017/tijtgat2017iccvw-embedded/}
}