Journal
REMOTE SENSING
Volume 11, Issue 18, Pages -Publisher
MDPI
DOI: 10.3390/rs11182144
Keywords
UAV; drone; autonomous UAV; UAS; remote sensing; deep learning; image processing; large-scale datasets; collision avoidance; obstacle detection
Categories
Funding
- Xunta de Galicia [ED431C 2016-045, ED431C 2016-047, ED431G/01]
- Centro singular de investigacion de Galicia accreditation 2016-2019 through the CONCORDANCE (Collision avOidaNCe fOR UAVs using Deep leArNing teChniques and optimized sEnsor design) Project [PC18/01]
- Agencia Estatal de Investigacion of Spain [TEC2016-75067-C4-1-R]
- ERDF funds of the EU (AEI/FEDER, UE)
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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.
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