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Sensors (Basel). 2019 Oct 7;19(19). pii: E4332. doi: 10.3390/s19194332.

Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning.

Author information

1
Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy. roberto.opromolla@unina.it.
2
Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy. giu.inch@gmail.com.
3
Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy. giancarmine.fasano@unina.it.

Abstract

The performance achievable by using Unmanned Aerial Vehicles (UAVs) for a large variety of civil and military applications, as well as the extent of applicable mission scenarios, can significantly benefit from the exploitation of formations of vehicles able to fly in a coordinated manner (swarms). In this respect, visual cameras represent a key instrument to enable coordination by giving each UAV the capability to visually monitor the other members of the formation. Hence, a related technological challenge is the development of robust solutions to detect and track cooperative targets through a sequence of frames. In this framework, this paper proposes an innovative approach to carry out this task based on deep learning. Specifically, the You Only Look Once (YOLO) object detection system is integrated within an original processing architecture in which the machine-vision algorithms are aided by navigation hints available thanks to the cooperative nature of the formation. An experimental flight test campaign, involving formations of two multirotor UAVs, is conducted to collect a database of images suitable to assess the performance of the proposed approach. Results demonstrate high-level accuracy, and robustness against challenging conditions in terms of illumination, background and target-range variability.

KEYWORDS:

UAV swarms; YOLO; deep learning; machine vision; unmanned aerial vehicles; visual detection; visual tracking

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