Implementation of an artificial neural network in recognizing in-flight quadrotor images

• Main Authors: Nakano, Reiichiro Christian S., Bandala, Argel A., Faelden, Gerard Ely, Maiiiiigo, Jose Martin, Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2016
• Subjects: Neural networks (Computer science); Quadrotor helicopters; Swarm intelligence; Electrical and Electronics; Systems and Communications
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/653
• Institution: De La Salle University

This paper shows an implementation of a feedforward artificial neural network capable of recognizing images of the CrazyFlie 2.0 quadrotor during flight. The network is to be used in a real-time quadrotor swarming application and has to be able to successfully differentiate pictures that show a quadrotor in flight versus pictures that do not. The network was trained using a standard backpropagation algorithm and images taken from a video of the said quadrotor in flight. These images were divided into three groups: A training set and validation set for the training stage, and a testing set for verification of the trained neural network. The results showed that the neural network was able to correctly identify the images in the testing phase 100 percent of the time while achieving a 94 percent accuracy for the images in the testing set. © 2015 IEEE.