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Open AccessReview

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

1
Department of Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain
2
Centro de investigación CITIC, Universidade da Coruña, 15071 A Coruña, Spain
3
Department of Computer Science, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2144; https://doi.org/10.3390/rs11182144
Received: 19 August 2019 / Revised: 7 September 2019 / Accepted: 10 September 2019 / Published: 14 September 2019
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications)
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. View Full-Text
Keywords: UAV; drone; autonomous UAV; UAS; remote sensing; deep learning; image processing; large-scale datasets; collision avoidance; obstacle detection UAV; drone; autonomous UAV; UAS; remote sensing; deep learning; image processing; large-scale datasets; collision avoidance; obstacle detection
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MDPI and ACS Style

Fraga-Lamas, P.; Ramos, L.; Mondéjar-Guerra, V.; Fernández-Caramés, T.M. A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance. Remote Sens. 2019, 11, 2144. https://doi.org/10.3390/rs11182144

AMA Style

Fraga-Lamas P, Ramos L, Mondéjar-Guerra V, Fernández-Caramés TM. A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance. Remote Sensing. 2019; 11(18):2144. https://doi.org/10.3390/rs11182144

Chicago/Turabian Style

Fraga-Lamas, Paula; Ramos, Lucía; Mondéjar-Guerra, Víctor; Fernández-Caramés, Tiago M. 2019. "A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance" Remote Sens. 11, no. 18: 2144. https://doi.org/10.3390/rs11182144

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