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"Search and rescue"
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Unmanned Aerial Vehicles for Search and Rescue: A Survey
In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR.
Journal Article
HH-60 Pave Hawk helicopters
The United States Air Force uses HH-60 Pave Hawks to deploy Special Forces and to perform search-and-rescue missions. Simple text and photos highlight how the speed and technology of these helicopters help them complete missions for the U.S. Air Force.
Book
An Enhanced Target Detection Algorithm for Maritime Search and Rescue Based on Aerial Images
Unmanned aerial vehicles (UAVs), renowned for their rapid deployment, extensive data collection, and high spatial resolution, are crucial in locating distressed individuals during search and rescue (SAR) operations. Challenges in maritime search and rescue include missed detections due to issues including sunlight reflection. In this study, we proposed an enhanced ABT-YOLOv7 algorithm for underwater person detection. This algorithm integrates an asymptotic feature pyramid network (AFPN) to preserve the target feature information. The BiFormer module enhances the model’s perception of small-scale targets, whereas the task-specific context decoupling (TSCODE) mechanism effectively resolves conflicts between localization and classification. Using quantitative experiments on a curated dataset, our model outperformed methods such as YOLOv3, YOLOv4, YOLOv5, YOLOv8, Faster R-CNN, Cascade R-CNN, and FCOS. Compared with YOLOv7, our approach enhances the mean average precision (mAP) from 87.1% to 91.6%. Therefore, our approach reduces the sensitivity of the detection model to low-lighting conditions and sunlight reflection, thus demonstrating enhanced robustness. These innovations have driven advancements in UAV technology within the maritime search and rescue domains.
Journal Article
Stella the search dog
\"Meet courageous canine Stella and follow along as she spends an exciting day at work with the police department ... [Tells] the true stories of brave Stella's role as a search dog in Virginia\"-- Provided by publisher.
Book
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques
Search and Rescue (SAR) missions represent an important challenge in the robotics research field as they usually involve exceedingly variable-nature scenarios which require a high-level of autonomy and versatile decision-making capabilities. This challenge becomes even more relevant in the case of aerial robotic platforms owing to their limited payload and computational capabilities. In this paper, we present a fully-autonomous aerial robotic solution, for executing complex SAR missions in unstructured indoor environments. The proposed system is based on the combination of a complete hardware configuration and a flexible system architecture which allows the execution of high-level missions in a fully unsupervised manner (i.e. without human intervention). In order to obtain flexible and versatile behaviors from the proposed aerial robot, several learning-based capabilities have been integrated for target recognition and interaction. The target recognition capability includes a supervised learning classifier based on a computationally-efficient Convolutional Neural Network (CNN) model trained for target/background classification, while the capability to interact with the target for rescue operations introduces a novel Image-Based Visual Servoing (IBVS) algorithm which integrates a recent deep reinforcement learning method named Deep Deterministic Policy Gradients (DDPG). In order to train the aerial robot for performing IBVS tasks, a reinforcement learning framework has been developed, which integrates a deep reinforcement learning agent (e.g. DDPG) with a Gazebo-based simulator for aerial robotics. The proposed system has been validated in a wide range of simulation flights, using Gazebo and PX4 Software-In-The-Loop, and real flights in cluttered indoor environments, demonstrating the versatility of the proposed system in complex SAR missions.
Journal Article
Saving Bravo : the greatest rescue mission in Navy SEAL history
\"The untold story of the most important rescue mission not just of the Vietnam War, but the entire Cold War: one American aviator, who knew our most important secrets, crashed behind enemy lines and was sought by the entire North Vietnamese and Russian military machines. One Navy SEAL and his Vietnamese partner had to sneak past them all to save him\"-- Provided by publisher.
Book
Deep Learning Approach in Aerial Imagery for Supporting Land Search and Rescue Missions
In this paper, we propose a novel approach to person detection in UAV aerial images for search and rescue tasks in Mediterranean and Sub-Mediterranean landscapes. Person detection in very high spatial resolution images involves target objects that are relatively small and often camouflaged within the environment; thus, such detection is a challenging and demanding task. The proposed method starts by reducing the search space through a visual attention algorithm that detects the salient or most prominent segments in the image. To reduce the number of non-relevant salient regions, we selected those regions most likely to contain a person using pre-trained and fine-tuned convolutional neural networks (CNNs) for detection. We established a special database called HERIDAL to train and test our model. This database was compiled for training purposes, and it contains over 68,750 image patches of wilderness acquired from an aerial perspective as well as approximately 500 labelled full-size real-world images intended for testing purposes. The proposed method achieved a detection rate of 88.9% and a precision of 34.8%, which demonstrates better effectiveness than the system currently used by Croatian Mountain search and rescue (SAR) teams (IPSAR), which is based on mean-shift segmentation. We also used the HERIDAL database to train and test a state-of-the-art region proposal network, Faster R-CNN (Ren et al. in Faster R-CNN: towards real-time object detection with region proposal networks, 2015. CoRR arXiv:1506.01497), which achieved comparable but slightly worse results than those of our proposed method.
Journal Article
A Multi-Robot Coverage Path Planning Method for Maritime Search and Rescue Using Multiple AUVs
In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets.
Journal Article