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

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Bibliographic Details
Title: Deep Learning-Based Drone Defense System for Autonomous Detection and Mitigation of Balloon-Borne Threats.
Authors: Kim, Joosung, Joe, Inwhee
Source: Electronics (2079-9292); Apr2025, Vol. 14 Issue 8, p1553, 20p
Subject Terms: OBJECT recognition (Computer vision), CONVOLUTIONAL neural networks, CIVIL defense, X-ray imaging, HAZARDOUS substances
Abstract: 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]
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Database: Complementary Index
Description
Abstract: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]
ISSN:20799292
DOI:10.3390/electronics14081553