On Automatic Person-in-Water Detection for Marine Search and Rescue Operations
Open access
Autor(in)
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Datum
2024Typ
- Journal Article
ETH Bibliographie
yes
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Abstract
In marine search and rescue missions, the objective is to find a missing person in the water. Time is a critical factor in the identification of the missing person, as any delay in locating them can have life-threatening consequences. Autonomous unmanned aerial vehicles (UAVs) possess the potential to help in the search task by providing a bird's-eye view helping to cover larger areas faster. Therefore, it is very important that UAVs can efficiently and accurately detect persons in the water. This work studies automatic person detection in the water from a UAV. We performed experiments on both lakes and sea near Turku, Finland, and captured videos of people in the water from various altitudes and different viewing angles. Our person-in-water detection tests focus on important factors that have not received sufficient attention in prior studies: evaluation metrics and detection thresholds, the impact and use of different bounding box sizes, multi-frame detection and performance in unseen environments. We provide analysis of the suitability of different approaches for the person detection task and we also publish our training and testing data that includes over 72000 frames. To the best of our knowledge, this is the largest publicly available person-in-water detection dataset. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000670532Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
IEEE AccessBand
Seiten / Artikelnummer
Verlag
IEEEThema
Search and rescue (SAR); person-in-water; unmanned aerial vehicle (UAV); object detection; deep learning (DL); datasetETH Bibliographie
yes
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