Paper Info
Title
Live Target Detection with Deep Learning Neural Network and Unmanned Aerial Vehicle on Android Mobile Device.
Abstract
This paper describes the stages faced during the development of an Android program which obtains and decodes live images from DJI Phantom 3 Professional Drone and implements certain features of the TensorFlow Android Camera Demo application. Test runs were made and outputs of the application were noted. A lake was classified as seashore, breakwater and pier with the proximities of 24.44%, 21.16% and 12.96% respectfully. The joystick of the UAV controller and laptop keyboard was classified with the proximities of 19.10% and 13.96% respectfully. The laptop monitor was classified as screen, monitor and television with the proximities of 18.77%, 14.76% and 14.00% respectfully. The computer used during the development of this study was classified as notebook and laptop with the proximities of 20.04% and 11.68% respectfully. A tractor parked at a parking lot was classified with the proximity of 12.88%. A group of cars in the same parking lot were classified as sports car, racer and convertible with the proximities of 31.75%, 18.64% and 13.45% respectfully at an inference time of 851ms.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Android (operating system),Parking lot,Laptop,Computer science,Real-time computing,Mobile device,Artificial intelligence,Drone,Deep learning,Joystick,Machine learning,Tractor
DocType
Volume
Citations 
Journal
abs/1803.07015
0
PageRank 
References 
Authors
0.34
2
3
Name
Order
Citations
PageRank
Ali Canberk Anar100.34
Erkan Bostanci2659.18
Mehmet Serdar Güzel344.46