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Deep Learning Techniques for Advanced Drone Detection Systems: A Comprehensive Review of Techniques, Challenges and Future Directions

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 12 No. 4 (2024) · hlm. 818–857

DOI
10.52549/ijeei.v12i4.6028
ISSN
2089-3272, 2089-3272
Terbit
2024-11-17
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

The widespread use of Unmanned Aerial Vehicles (UAVs), commonly known as drones, across various sectors, such as civilian, commercial, and military operations, has created significant challenges in ensuring security, safety, and privacy. This paper provides a comprehensive review of the latest advancements in drone detection systems leveraging deep learning techniques, covering the period from 2020 to 2024. It critically evaluates both optical (visible light and thermal infrared) and non-optical (radio frequency, radar, and acoustic) detection methodologies. The analysis includes cutting-edge models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), focusing on their application in drone detection. Key challenges like real-time processing, environmental interference, and differentiation between drones and similar objects are examined. Potential solutions, including sensor fusion, attention mechanisms, and the integration of emerging technologies such as the Internet of Things (IoT) and 5G networks, are discussed in detail. The paper concludes with future research directions to enhance drone detection systems' robustness, scalability, and accuracy, particularly in complex and dynamic environments. This review offers valuable insights for researchers and industry professionals working towards next-generation drone detection technologies.

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  • International Islamic University Malaysia
  • Yogyakarta State University
  • Telkom University
  • Birzeit University
  • Özyeğin University
  • Princess of Naradhiwas University
  • Universitas Gadjah Mada
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Rekan penulisOrganisasiArtikel bersama
Fatchul ArifinYogyakarta State University10
Ali SophianInternational Islamic University Malaysia6
Arselan AshrafInternational Islamic University Malaysia6
Mohamed Hadi HabaebiInternational Islamic University Malaysia6
Ahmed Samir Abed BadawiInternational Islamic University Malaysia4
Sheroz KhanInternational Islamic University Malaysia4
Hasmah MansorInternational Islamic University Malaysia4
Suriza Ahmad ZabidiInternational Islamic University Malaysia4
Mashkuri YaacobInternational Islamic University Malaysia4
Nurul Fadzlin HasbullahInternational Islamic University Malaysia3
Aisha HashimAfiliasi tidak diketahui3
Alhareth ZyoudBirzeit University3
Malik Arman MorshidiInternational Islamic University Malaysia3
Mohd Shahrin Abu HanifahInternational Islamic University Malaysia3
Noreha Abdul MalikInternational Islamic University Malaysia2
Rashidah Funke OlanrewajuInternational Islamic University Malaysia2
Mohammed ElamassieÖzyeğin University2
Aris NasuhaYogyakarta State University2
Anggun WinursitoYogyakarta State University2
Ardy Seto PriambodoYogyakarta State University2
Majdee TohtayongPrincess of Naradhiwas University2
Nur Shahida MidiInternational Islamic University Malaysia2
Diyah PutrianiUniversitas Gadjah Mada2
Gairuzazmi Mat GhaniInternational Islamic University Malaysia2
Amart SulongInternational Islamic University Malaysia2
Othman Omran KhalifaInternational Islamic University Malaysia2
Hassan DaoUniversity of Kuala Lumpur2
Abdul MutholibInternational Islamic University Malaysia2
Intan Rahmithul Husna YaldiInternational Islamic University Malaysia2
Yasmin Mahira Saiful MunirInternational Islamic University Malaysia2
Tika ArundinaUniversity of Indonesia2
Mohd Azmi OmarInternational Centre for Education in Islamic Finance2
Nur Atikah Muhamat SalehInternational Islamic University Malaysia2
W. IdrisInternational Islamic University Malaysia1

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Sumber metadata: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Deep Learning Techniques for Advanced Drone Detection Systems: A Comprehensive Review of Techniques, Challenges and Future Directions</dc:title>
	<dc:creator xml:lang="en">Muhamad Zamri, Fatin Najihah</dc:creator>
	<dc:creator xml:lang="en">Gunawan, Teddy Surya</dc:creator>
	<dc:creator xml:lang="en">Kartiwi, Mira</dc:creator>
	<dc:creator xml:lang="en">Pratondo, Agus</dc:creator>
	<dc:creator xml:lang="en">Yusoff, Siti Hajar</dc:creator>
	<dc:creator xml:lang="en">Mohd. Mustafah, Yasir</dc:creator>
	<dc:subject xml:lang="en">Drone detection</dc:subject>
	<dc:subject xml:lang="en">Deep learning</dc:subject>
	<dc:subject xml:lang="en">Sensor fusion</dc:subject>
	<dc:subject xml:lang="en">Real-time processing</dc:subject>
	<dc:subject xml:lang="en">UAV security</dc:subject>
	<dc:subject xml:lang="en">Thermal infrared imaging</dc:subject>
	<dc:subject xml:lang="en">Radio frequency detection</dc:subject>
	<dc:subject xml:lang="en">Radar-based detection</dc:subject>
	<dc:subject xml:lang="en">Acoustic-based detection</dc:subject>
	<dc:description xml:lang="en">The widespread use of Unmanned Aerial Vehicles (UAVs), commonly known as drones, across various sectors, such as civilian, commercial, and military operations, has created significant challenges in ensuring security, safety, and privacy. This paper provides a comprehensive review of the latest advancements in drone detection systems leveraging deep learning techniques, covering the period from 2020 to 2024. It critically evaluates both optical (visible light and thermal infrared) and non-optical (radio frequency, radar, and acoustic) detection methodologies. The analysis includes cutting-edge models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), focusing on their application in drone detection. Key challenges like real-time processing, environmental interference, and differentiation between drones and similar objects are examined. Potential solutions, including sensor fusion, attention mechanisms, and the integration of emerging technologies such as the Internet of Things (IoT) and 5G networks, are discussed in detail. The paper concludes with future research directions to enhance drone detection systems' robustness, scalability, and accuracy, particularly in complex and dynamic environments. This review offers valuable insights for researchers and industry professionals working towards next-generation drone detection technologies.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2024-11-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://section.iaesonline.com/index.php/IJEEI/article/view/6028</dc:identifier>
	<dc:identifier>10.52549/ijeei.v12i4.6028</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 12 No. 4: December 2024; 818-857</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/6028/952</dc:relation>
</oai_dc:dc>