Vol.1, No.1, 2019, pp.1-8, doi:10.32604/jai.2019.06064
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ARTICLE
Intelligent Mobile Drone System Based on Real-Time Object Detection
  • Chuanlong Li1,2, Xingming Sun1,2,*, Junhao Cai3,*
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China.
Jiangsu Engineering Centre of Network Monitoring, Nanjing, 210044, China.
School of Politics, Economics and International Relations, University of Reading, Berkshire, RG6 6BG, United Kingdom.
*Corresponding Authors: Xingming Sun. Email: . Junhao Cai. Email: .
Abstract
Drone also known as unmanned aerial vehicle (UAV) has drawn lots of attention in recent years. Quadcopter as one of the most popular drones has great potential in both industrial and academic fields. Quadcopter drones are capable of taking off vertically and flying towards any direction. Traditional researches of drones mainly focus on their mechanical structures and movement control. The aircraft movement is usually controlled by a remote controller manually or the trajectory is pre-programmed with specific algorithms. Consumer drones typically use mobile device together with remote controllers to realize flight control and video transmission. Implementing different functions on mobile devices can result in different behaviors of drones indirectly. With the development of deep learning in computer vision field, commercial drones equipped with camera can be much more intelligent and even realize autonomous flight. In the past, running deep learning based algorithms on mobile devices is highly computational intensive and time consuming. This paper utilizes a novel real-time object detection method and deploys the deep learning model on the modern mobile device to realize autonomous object detection and object tracking of drones.
Keywords
Drone, UAV, CNN, object detection, mobile application, Ios.
Cite This Article
C. . Li, X. . Sun and J. . Cai, "Intelligent mobile drone system based on real-time object detection," Journal on Artificial Intelligence, vol. 1, no.1, pp. 1–8, 2019.
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