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Proceedings Paper

Vision-based deep learning for UAVs collaboration
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Paper Abstract

Unmanned Aerial Vehicles (UAVs) are very popular and increasingly used in different applications. For many applications, it can be very interesting to achieve UAVs collaboration. In this work, we propose the use of vision-based collaboration between UAVs. The proposed approach uses images captured by a UAV and deep learning to detect and follow another UAV. To detect the leader UAV, we developed an approach based on the deep YOLO algorithm. This approach was able to process videos at 30 fps and get high mAP for UAV detection. To follow the leader UAV, we developed a high-level control algorithm based on the use of the detected bounding box coordinates. The bounding box size and position help compute the command to send to the follower UAV. Tests were conducted in outdoor scenarios using quadcopter UAVs. The obtained results and the high mAP are promising and show the possibility of using this kind of vision-based deep learning approach for UAVs collaboration.

Paper Details

Date Published: 15 May 2019
PDF: 10 pages
Proc. SPIE 11021, Unmanned Systems Technology XXI, 1102108 (15 May 2019); doi: 10.1117/12.2519875
Show Author Affiliations
Sébastien Arola, Univ. de Moncton (France)
UPSSITECH, Univ. Toulouse (France)
Moulay A. Akhloufi, Univ. de Moncton (Canada)

Published in SPIE Proceedings Vol. 11021:
Unmanned Systems Technology XXI
Charles M. Shoemaker; Hoa G. Nguyen; Paul L. Muench, Editor(s)

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