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Drones
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Innovative Applications of UAVs in Search and Rescue: Improving Safety...
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Innovative Applications of UAVs in Search and Rescue: Improving Safety and Effectiveness

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1000

Special Issue Editors

Dr. Kristian Amadori

E-Mail Website
Guest Editor
Department of Management and Engineering, Linköping University, Stockholm, Sweden
Interests: mechanical engineering
Dr. Christopher Jouannet

E-Mail Website
Guest Editor
Department of Management and Engineering, Linköping University, Stockholm, Sweden
Interests: aeronautics; aerodynamics; aircraft conceptual design; mdo; simulation

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have seen considerable development in recent years. Their increasing employment across diverse applications is frequently reported in both specialized publications as well as in public media flows. Among all possible missions, Search and Rescue (SAR) address the fundamental need to protect and preserve human life during emergencies and disasters. UAVs have the potential to transform the landscape of search and rescue (SAR) operations, offering real-time situational awareness, rapid deployment, and minimized risk to human responders. From natural disaster response to complex urban rescue missions, drone technology could deliver unprecedented capabilities for surveying hazardous areas, detecting survivors, and supporting emergency personnel in time-critical scenarios. Despite these advances, challenges persist regarding flight endurance, sensor integration, regulatory frameworks, and coordinated multi-drone operations.

This Special Issue of Drones (MDPI) invites original research papers, review articles, and case studies showcasing innovative UAV-based SAR strategies, System-of-Systems (SoS) and system architectures, and field deployments. Submissions should explore novel techniques that improve system architecture exploration, efficiency and mission effectiveness, reduce operational costs, and enhance responder safety. Interdisciplinary contributions drawing on robotics, computer vision, artificial intelligence, and communications are particularly welcome.

Potential themes include, but are not limited to:

* System of Systems simulation, analysis and evaluation
* System of Systems architecting and architecture alternatives exploration
* Advanced sensing and imaging: Thermal, hyperspectral, or LiDAR applications for improved detection and mapping
* Concepts of Operation, navigation and control: mission and task planning, AI-driven algorithms for obstacle avoidance, path planning, and swarm coordination
* Manned-Unmanned teaming: interoperability, communication and situational awareness, ergonomic interfaces and decision-support systems for seamless coordination with ground crews
* Real-time data processing: Machine learning and edge computing methods for on-board situational assessment
* Regulatory and ethical considerations: Airspace integration, privacy, and societal implications of UAV deployment

Dr. Kristian Amadori
Dr. Christopher Jouannet
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • search and rescue (SAR)
  • unmanned aerial vehicles (UAVs)
  • drone technology
  • system of systems (SoS)
  • sensor integration
  • real-time data processing
  • multi-UAV Coordination
  • autonomy and AI
  • situational awareness
  • flight safety and regulation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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22 pages, 6496 KiB  
Article
Real-Time Search and Rescue with Drones: A Deep Learning Approach for Small-Object Detection Based on YOLO
by Francesco Ciccone and Alessandro Ceruti
Drones 2025, 9(8), 514; https://doi.org/10.3390/drones9080514 - 22 Jul 2025
Abstract
Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object sizes, cluttered backgrounds, and limited onboard computational resources, especially [...] Read more.
Unmanned aerial vehicles are increasingly used in civil Search and Rescue operations due to their rapid deployment and wide-area coverage capabilities. However, detecting missing persons from aerial imagery remains challenging due to small object sizes, cluttered backgrounds, and limited onboard computational resources, especially when managed by civil agencies. In this work, we present a comprehensive methodology for optimizing YOLO-based object detection models for real-time Search and Rescue scenarios. A two-stage transfer learning strategy was employed using VisDrone for general aerial object detection and Heridal for Search and Rescue-specific fine-tuning. We explored various architectural modifications, including enhanced feature fusion (FPN, BiFPN, PB-FPN), additional detection heads (P2), and modules such as CBAM, Transformers, and deconvolution, analyzing their impact on performance and computational efficiency. The best-performing configuration (YOLOv5s-PBfpn-Deconv) achieved a mAP@50 of 0.802 on the Heridal dataset while maintaining real-time inference on embedded hardware (Jetson Nano). Further tests at different flight altitudes and explainability analyses using EigenCAM confirmed the robustness and interpretability of the model in real-world conditions. The proposed solution offers a viable framework for deploying lightweight, interpretable AI systems for UAV-based Search and Rescue operations managed by civil protection authorities. Limitations and future directions include the integration of multimodal sensors and adaptation to broader environmental conditions. Full article
(This article belongs to the Special Issue Innovative Applications of UAVs in Search and Rescue: Improving Safety and Effectiveness)
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19 pages, 3520 KiB  
Article
Vision-Guided Maritime UAV Rescue System with Optimized GPS Path Planning and Dual-Target Tracking
by Suli Wang, Yang Zhao, Chang Zhou, Xiaodong Ma, Zijun Jiao, Zesheng Zhou, Xiaolu Liu, Tianhai Peng and Changxing Shao
Drones 2025, 9(7), 502; https://doi.org/10.3390/drones9070502 - 16 Jul 2025
Viewed by 346
Abstract
With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven [...] Read more.
With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. Full article
(This article belongs to the Special Issue Innovative Applications of UAVs in Search and Rescue: Improving Safety and Effectiveness)
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