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A Large-Scale Uav Audio Dataset and ...

Wang, Yaqin.

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A Large-Scale Uav Audio Dataset and Audio-Based UAV Classification Using CNN.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Large-Scale Uav Audio Dataset and Audio-Based UAV Classification Using CNN./
Author:	Wang, Yaqin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	119 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: A.
Contained By:	Dissertations Abstracts International85-05A.
Subject:	Unmanned aerial vehicles. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30685607
ISBN:	9798380727259

A Large-Scale Uav Audio Dataset and Audio-Based UAV Classification Using CNN.
Wang, Yaqin.

A Large-Scale Uav Audio Dataset and Audio-Based UAV Classification Using CNN. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 119 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: A.

Thesis (Ph.D.)--Purdue University, 2023.

The growing popularity and increased accessibility of unmanned aerial vehicles (UAVs) have raised concerns about potential threats they may pose. In response, researchers have devoted significant eorts to developing UAV detection and classification systems, utilizing diverse methodologies such as computer vision, radar, radio frequency, and audiobased approaches. However, the availability of publicly accessible UAV audio datasets remains limited. Consequently, this research endeavor was undertaken to address this gap by undertaking the collection of a comprehensive UAV audio dataset, alongside the development of a precise and ecient audio-based UAV classification system.This research project is structured into three distinct phases, each serving a unique purpose in data collection and training the proposed UAV classifier. These phases encompass data collection, dataset evaluation, the implementation of a proposed convolutional neural network, training procedures, as well as an in-depth analysis and evaluation of the obtained results. To assess the eectiveness of the model, several evaluation metrics are employed, including training accuracy, loss rate, the confusion matrix, and ROC curves.The findings from this study conclusively demonstrate that the proposed CNN classifier exhibits nearly flawless performance in accurately classifying UAVs across 22 distinct categories.

ISBN: 9798380727259Subjects--Topical Terms:

3560267
Unmanned aerial vehicles.

A Large-Scale Uav Audio Dataset and Audio-Based UAV Classification Using CNN.
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