A Feature Engineering Focused System for Acoustic UAV Payload Detection

Yaqin Wang, Facundo Fagiani, Kar Ho, Eric Matson

2022

Abstract

The technology evolution of Unmanned Aerial Vehicles (UAVs) or drones, has made these devices suitable for a wide new range of applications, but it has also raised safety concerns as drones can be used for carrying explosives or weapons with malicious intentions. In this paper, Machine Learning (ML) algorithms are used to identify drones carrying payloads based on the sound signals they emit. We evaluate and propose a feature-based classification. Five individual features, and one combinations of features are used to train four different standard machine learning models: SupportVector Machine (SVM), Gaussian Naive Bayes (GNB), K-Nearest Neighbor (KNN) and a Neural Network (NN) model. The training and testing dataset is composed of sound samples of loaded drones and unloaded drones collected by the team. The results show that the combination of features outperforms the individual ones, with much higher accuracy scores.

Paper Citation

in Harvard Style

Wang Y., Fagiani F., Ho K. and Matson E. (2022). A Feature Engineering Focused System for Acoustic UAV Payload Detection. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 470-475. DOI: 10.5220/0010843800003116

in Bibtex Style

@conference{icaart22,
author={Yaqin Wang and Facundo Fagiani and Kar Ho and Eric Matson},
title={A Feature Engineering Focused System for Acoustic UAV Payload Detection},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={470-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010843800003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - A Feature Engineering Focused System for Acoustic UAV Payload Detection
SN - 978-989-758-547-0
AU - Wang Y.
AU - Fagiani F.
AU - Ho K.
AU - Matson E.
PY - 2022
SP - 470
EP - 475
DO - 10.5220/0010843800003116