We present a convolutional neural network (CNN) that identifies drone models in real-life
videos. The neural network is trained on synthetic images and tested on a real-life dataset of …

The growing ubiquity of drones has raised concerns over the ability of traditional air-space
monitoring technologies to accurately characterise such vehicles. Here, we present a CNN …

Classifiers using convolutional neural networks (CNNs) often yield high accuracies on
samples that come from the same distribution as the training data. In this study we evaluate …

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 …

We propose a drone classification method based on convolutional neural network (CNN)
and micro-Doppler signature (MDS). The MDS only presents Doppler information in time …

Featured Application An application of this research is UAV identification (in terms of make
and model) based upon a malicious UAV report that includes a photo of the suspicious UAV …

Real-time scene understanding is important for many applications of Unmanned Aerial
Vehicles (UAVs) such as reconnaissance, surveillance, mapping, and infrastructure …

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an
emerging application attracting significant attention from researchers in various areas of …

There are numerous applications of unmanned aerial vehicles (UAVs) in the management of
civil infrastructure assets. A few examples include routine bridge inspections, disaster …

A large amount of training image data is required for solving image classification problems
using deep learning (DL) networks. In this study, we aimed to train DL networks with …