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

• Published in: Journal of sensors Vol. 2017; no. 2017; pp. 1 - 13
• Main Authors: Campoy, Pascual, Rodriguez-Ramos, Alejandro, Sampedro, Carlos, Carrio, Adrian
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Automation; Communications systems; Computers; Data acquisition; Deep learning; Information processing; International conferences; Neural networks; Pattern recognition; Position (location); Representations; Robotics; Robots; Sensors; Taxonomy; Unmanned aerial vehicles
• ISSN: 1687-725X; 1687-7268
• DOI: 10.1155/2017/3296874

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.