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September 10, 2025Journal of Internet Services and Information SecurityOpen Access

Real Time Drone Detection Based on Acoustics Using Hybrid Deep Learning Models

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Authors

SJShahad W JasimSHSaad S. Hreshee

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Overview

Hybrid models improve drone detection accuracy through acoustic signals, suggesting a solution for RF limitations.

Key Points

  • The hybrid deep learning model achieved a maximum accuracy of 98% for drone detection.
  • A custom dataset of 1,500 audio clips included recordings from DJI Phantom 4 Pro and Mavic 2 Pro drones.
  • The CNN-Conformer model effectively captured spatial and long-term features, enhancing detection robustness.
  • Acoustic detection offers a promising solution where visual and RF detection methods may fail.

Cite This Study

Jasim et al. (2025) studied this question.

synapsesocial.com/papers/68c1afb954b1d3bfb60e6f75https://doi.org/10.58346/jisis.2025.i2.046
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks2024 · 43 citations
  2. 2From Sound to Sight: Audio-Visual Fusion and Deep Learning for Drone Detection2024 · 20 citations
  3. 3Acoustic Source Drone Detection System Using Tetrahedral Microphone Array and Deep Neural Networks2026 · 2 citations
  4. 4Deep acoustic learning on unmanned aerial vehicles for real-time human and drone detection2026 · 4 citations
  5. 5Drone detection with radio frequency signals and deep learning models2024 · 1 citations