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In today’s rapidly evolving society, the swift construction of high-rise buildings has been accompanied by an increased risk of incidents that jeopardize human safety. Emergencies—such as fires or structural collapses—in these tall structures pose significant challenges, as compromised communication systems and obstructed GPS signals often hinder external access. Consequently, employing devices that operate independently of local infrastructures is essential. However, enabling these devices to navigate and detect victims in post-incident environments autonomously—characterized by altered spatial configurations and numerous obstacles—remains a formidable challenge. The necessity for efficient and timely search and rescue operations in indoor or non-GPS environments, such as factories, warehouses, and high-rise buildings, is growing worldwide. Traditional rescue methods in these constrained and complex settings are time-consuming and hazardous, underscoring the demand for more autonomous, technology-driven solutions. Unmanned Aerial Vehicles (UAVs), or drones, have emerged as a promising technology in this regard, offering both a strategic aerial perspective and agile maneuverability. This study presents an integrated UAV system powered by deep learning to enhance search and rescue operations in GPS-denied in-building environments. The primary objectives are to enable autonomous navigation and real-time victim detection within complex, obstacle-ridden settings. We developed a ResNet-8-based model for efficient obstacle avoidance to meet these goals and employed several YOLO-based architectures to localize victims accurately. Extensive experiments in a real in-building environment demonstrated that the navigation model consistently achieved over 90% accuracy with processing times under 17 ms, while the detection models operated at frame rates up to 45 FPS. The results confirm that the proposed system enhances the safety and efficiency of rescue missions and offers a robust solution for emergency operations in environments where traditional GPS-based navigation is not feasible.

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Drone-Based Autonomous Navigation and Victim Detection Using Deep Learning Technique for Non-GPS In-Building Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Trong Tuan Do,
  • Ba Dong Nguyen,
  • Dinh Duy Nguyen,
  • Duc Minh Nguyen,
  • Thanh Trung Nguyen,
  • Quang Khiem Nguyen

摘要

In today’s rapidly evolving society, the swift construction of high-rise buildings has been accompanied by an increased risk of incidents that jeopardize human safety. Emergencies—such as fires or structural collapses—in these tall structures pose significant challenges, as compromised communication systems and obstructed GPS signals often hinder external access. Consequently, employing devices that operate independently of local infrastructures is essential. However, enabling these devices to navigate and detect victims in post-incident environments autonomously—characterized by altered spatial configurations and numerous obstacles—remains a formidable challenge. The necessity for efficient and timely search and rescue operations in indoor or non-GPS environments, such as factories, warehouses, and high-rise buildings, is growing worldwide. Traditional rescue methods in these constrained and complex settings are time-consuming and hazardous, underscoring the demand for more autonomous, technology-driven solutions. Unmanned Aerial Vehicles (UAVs), or drones, have emerged as a promising technology in this regard, offering both a strategic aerial perspective and agile maneuverability. This study presents an integrated UAV system powered by deep learning to enhance search and rescue operations in GPS-denied in-building environments. The primary objectives are to enable autonomous navigation and real-time victim detection within complex, obstacle-ridden settings. We developed a ResNet-8-based model for efficient obstacle avoidance to meet these goals and employed several YOLO-based architectures to localize victims accurately. Extensive experiments in a real in-building environment demonstrated that the navigation model consistently achieved over 90% accuracy with processing times under 17 ms, while the detection models operated at frame rates up to 45 FPS. The results confirm that the proposed system enhances the safety and efficiency of rescue missions and offers a robust solution for emergency operations in environments where traditional GPS-based navigation is not feasible.