Towards an Autonomous UAV-based System to Assist Search and Rescue Operations in Man Overboard Incidents

Feraru, Valeria Alexandra; Andersen, Rasmus Eckholdt; Boukas, Evangelos

doi:10.1109/ssrr50563.2020.9292632

Cited by 34 publications

(15 citation statements)

References 18 publications

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“…Nevertheless, the SeaDronesSee dataset is an excellent reference for the task of human detection and tracking in the marine environment that we plan to use in the future, at least for some classes, in conjunction with our MOBDrone for training and testing deep neural networks. Finally, Ferau et al in [11] faced the problem of assisting SAR operations in MOB incidents using autonomous UAV-based systems. Unlike our work, they aim to locate people in the water by analyzing images recorded with thermal instead of video cameras.…”

Section: Related Workmentioning

confidence: 99%

MOBDrone: a Drone Video Dataset for Man OverBoard Rescue

Cafarelli¹,

Ciampi²,

Vadicamo³

et al. 2022

Preprint

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Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can be leveraged for the automatic understanding of visual data acquired from drones. However, detecting people at sea in aerial imagery is challenging primarily due to the lack of specialized annotated datasets for training and testing detectors for this task. To fill this gap, we introduce and publicly release the MOBDrone benchmark, a collection of more than 125K drone-view images in a marine environment under several conditions, such as different altitudes, camera shooting angles, and illumination. We manually annotated more than 180K objects, of which about 113K man overboard, precisely localizing them with bounding boxes. Moreover, we conduct a thorough performance analysis of several state-of-the-art object detectors on the MOBDrone data, serving as baselines for further research.

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Section: Related Workmentioning

confidence: 99%

MOBDrone: a Drone Video Dataset for Man OverBoard Rescue

Cafarelli¹,

Ciampi²,

Vadicamo³

et al. 2022

Preprint

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“…It is used for navigation under low-sight conditions [18] (e.g., smoke) and to detect sources of fire and thermal hot spots [1]. Robotic systems equipped with thermal cameras also facilitate object detection [19] and search for casualties [20].…”

Section: Introductionmentioning

confidence: 99%

Aerial 3D Mapping with Continuous Time ICP for Urban Search and Rescue

Lauterbach¹,

Nüchter²

2023

Autonomous Mobile Mapping Robots

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Fast reconnaissance is essential for strategic decisions during the immediate response phase of urban search and rescue missions. Nowadays, UAVs with their advantageous overview perspective are increasingly used for reconnaissance besides manual inspection of the scenario. However, data evaluation is often limited to visual inspection of images or video footage. We present our LiDAR-based aerial 3D mapping system, providing real-time maps of the environment. UAV-borne laser scans typically offer a reduced field of view. Moreover, UAV trajectories are more flexible and dynamic compared to those of ground vehicles, for which SLAM systems are often designed. We address these challenges by a two-step registration approach based on continuous time ICP. The experiments show that the resulting maps accurately represent the environment.

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“…Use of uncrewed aerial vehicles (UAVs) by emergency services in civil applications is rapidly increasing (1), also for search and rescue (SAR) missions (2)(3)(4)(5)(6)(7). In contrast to crewed aircraft, such as helicopters, drones are more flexible and cheaper in acquisition, maintenance, and operation; moreover, they avoid risks to pilots under difficult weather conditions.…”

Section: Introductionmentioning

confidence: 99%

An autonomous drone for search and rescue in forests using airborne optical sectioning

2021

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Autonomous drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found, in total, 38 of 42 hidden persons. For experiments with predefined flight paths, the average precision was 86%, and we found 30 of 34 cases. For adaptive sampling experiments (where potential findings are double-checked on the basis of initial classification confidences), all eight hidden persons were found, leading to an average precision of 100%, whereas classification confidence was increased on average by 15%. Thermal image processing, classification, and dynamic flight path adaptation are computed on-board in real time and while flying. We show that deep learning–based person classification is unaffected by sparse and error-prone sampling within straight flight path segments. This finding allows search missions to be substantially shortened and reduces the image complexity to 1/10th when compared with previous approaches. The goal of our adaptive online sampling technique is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, because it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.

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Towards an Autonomous UAV-based System to Assist Search and Rescue Operations in Man Overboard Incidents

Cited by 34 publications

References 18 publications

MOBDrone: a Drone Video Dataset for Man OverBoard Rescue

MOBDrone: a Drone Video Dataset for Man OverBoard Rescue

Aerial 3D Mapping with Continuous Time ICP for Urban Search and Rescue

An autonomous drone for search and rescue in forests using airborne optical sectioning

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