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Deep Learning-Based Drone Defense System for Autonomous Detection and Mitigation of Balloon-Borne Threats.

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Název: Deep Learning-Based Drone Defense System for Autonomous Detection and Mitigation of Balloon-Borne Threats.
Autoři: Kim, Joosung, Joe, Inwhee
Zdroj: Electronics (2079-9292); Apr2025, Vol. 14 Issue 8, p1553, 20p
Témata: OBJECT recognition (Computer vision), CONVOLUTIONAL neural networks, CIVIL defense, X-ray imaging, HAZARDOUS substances
Abstrakt: In recent years, balloon-borne threats carrying hazardous or explosive materials have emerged as a novel form of asymmetric terrorism, posing serious challenges to public safety. In response to this evolving threat, this study presents an AI-driven autonomous drone defense system capable of real-time detection, tracking, and neutralization of airborne hazards. The proposed framework integrates state-of-the-art deep learning models, including YOLO (You Only Look Once) for fast and accurate object detection, and convolutional neural networks (CNNs) for X-ray image analysis, enabling precise identification of hazardous payloads. This multi-stage system ensures safe interception and retrieval while minimizing the risk of secondary damage from debris dispersion. Moreover, a robust data collection and storage architecture supports continuous model improvement, ensuring scalability and adaptability for future counter-terrorism operations. As balloon-based threats represent a new and unconventional security risk, this research offers a practical and deployable solution. Beyond immediate applicability, the system also provides a foundational platform for the development of next-generation autonomous security infrastructures in both civilian and defense contexts. [ABSTRACT FROM AUTHOR]
Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Deep Learning-Based Drone Defense System for Autonomous Detection and Mitigation of Balloon-Borne Threats.
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  Data: Electronics (2079-9292); Apr2025, Vol. 14 Issue 8, p1553, 20p
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  Data: <searchLink fieldCode="DE" term="%22OBJECT+recognition+%28Computer+vision%29%22">OBJECT recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22CONVOLUTIONAL+neural+networks%22">CONVOLUTIONAL neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22CIVIL+defense%22">CIVIL defense</searchLink><br /><searchLink fieldCode="DE" term="%22X-ray+imaging%22">X-ray imaging</searchLink><br /><searchLink fieldCode="DE" term="%22HAZARDOUS+substances%22">HAZARDOUS substances</searchLink>
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  Data: In recent years, balloon-borne threats carrying hazardous or explosive materials have emerged as a novel form of asymmetric terrorism, posing serious challenges to public safety. In response to this evolving threat, this study presents an AI-driven autonomous drone defense system capable of real-time detection, tracking, and neutralization of airborne hazards. The proposed framework integrates state-of-the-art deep learning models, including YOLO (You Only Look Once) for fast and accurate object detection, and convolutional neural networks (CNNs) for X-ray image analysis, enabling precise identification of hazardous payloads. This multi-stage system ensures safe interception and retrieval while minimizing the risk of secondary damage from debris dispersion. Moreover, a robust data collection and storage architecture supports continuous model improvement, ensuring scalability and adaptability for future counter-terrorism operations. As balloon-based threats represent a new and unconventional security risk, this research offers a practical and deployable solution. Beyond immediate applicability, the system also provides a foundational platform for the development of next-generation autonomous security infrastructures in both civilian and defense contexts. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
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  Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: OBJECT recognition (Computer vision)
        Type: general
      – SubjectFull: CONVOLUTIONAL neural networks
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      – SubjectFull: X-ray imaging
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              Text: Apr2025
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