# Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
## Appendix B Research Pool--2019 Section  
* [PERSON] et al. (2020) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images. _Drones_**2020**, \(4\), 7. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] FastDepth: Fast Monocular Depth Estimation on Embedded Systems. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20-24 May 2019; pp. 6101-6108. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Beauty and the beast: Optimal methods meet learning for drone racing. In Proceedings of the IEEE International Conference on Robotics and Automation, Montreal, QC, Canada, 20-24 May 2019; Volume 2019, pp. 690-696. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON]J. Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices. _Sensors_**2019**, _19_, 3371. [CrossRef] [PubMed]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Dav positioning for throughput maximization using deep learning approaches. _Sensors_**2019**, _19_, 2775. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones. In Proceedings of the 2019 53 rd Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 20-22 March 2019; pp. 1-6. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Deep Attention Neural Network for Multi-Label Classification in Unmanned Aerial Vehicle Imagery. _IEEE Access_**2019**, \(7\), 119873-119880. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] DroNet: Learning to Fly by Driving. _IEEE Robot. Autom. Lett._**2018**, \(3\), 1088-1095. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Deep Drone Racing: Learning Agile Flight in Dynamic Environments. In Proceedings of the Conference on Robotic Learning, Zurich, Switzerland, 29-31 October 2018; pp. 1-13. * [PERSON] et al. (2018) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. _Drones_**2018**, \(2\), 39. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning. _IEEE Robot. Autom. Lett._**2018**, \(3\), 2539-2544. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] System of autonomous navigation of the drone in difficult conditions of the forest trails. In Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2018, Moscow and St. Petersburg, Russia, 29 January-1 February 2018; Volume 2018, pp. 1036-1039. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Vision-Based Autonomous Landing of a Multi-Copter Unmanned Aerial Vehicle using Reinforcement Learning. In Proceedings of the 2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018, Dallas, TX, USA, 12-15 June 2018; pp. 108-114. [CrossRef]
* [PERSON] et al. (2018) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A deep learning approach towards autonomous flight in forest environments. In Proceedings of the 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP), Cholula, Mexico, 21-23 February 2018; pp. 139-144. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Learning to fly by crashing.