Convolutional Neural Networks for Drone Model Classification

This work establishes performances for drone group (fixed wing vs rotary wing), drone subgroup (fixed wing vs hexacopter vs quadcopter) and drone model classification using a convolutional neural network (CNN). Data is from an experimental campaign with nine different drone models flying along various trajectories. It is demonstrated that CNNs are very capable of drone recognition, with baseline performances as high as 98%.