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Journal of Sensors
Volume 2017, Article ID 3296874, 13 pages
Review Article

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Computer Vision and Aerial Robotics Group, Centre for Automation and Robotics (CAR) UPM-CSIC, Universidad Politécnica de Madrid, Calle José Gutiérrez Abascal 2, 28006 Madrid, Spain

Correspondence should be addressed to Adrian Carrio; se.mpu@oirrac.nairda

Received 28 April 2017; Accepted 18 June 2017; Published 14 August 2017

Academic Editor: Vera Tyrsa

Copyright © 2017 Adrian Carrio et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.