Generating synthetic data for deep learning-based drone detection

Drone detection is an important yet challenging task in the context of object detection. The development of robust and reliable drone detection systems requires large amounts of labeled data, especially when using deep learning (DL) models. Unfortunately, acquiring real data is expensive, time-consu...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Dieter, Tamara, Weinmann, Andreas, Brucherseifer, Eva
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Drone detection is an important yet challenging task in the context of object detection. The development of robust and reliable drone detection systems requires large amounts of labeled data, especially when using deep learning (DL) models. Unfortunately, acquiring real data is expensive, time-consuming, and often limited by external factors. This makes synthetic data a promising approach to addressing data deficiencies. In this paper, we present a data generation pipeline based on Unreal Engine 4.25 and Microsoft AirSim, designed to create synthetic labeled data for drone detection using three-dimensional environments. As part of an ablation study, we investigate the potential use of synthetic data in drone detection by analyzing different training strategies, influencing factors, and data generation parameters, specifically related to the visual appearance of a drone.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0180345