A1 Journal article (refereed)
On Automatic Person-in-Water Detection for Marine Search and Rescue Operations (2024)

Taipalmaa, J., Raitoharju, J., Queralta, J. P., Westerlund, T., & Gabbouj, M. (2024). On Automatic Person-in-Water Detection for Marine Search and Rescue Operations. IEEE Access, 12, 52428-52438. https://doi.org/10.1109/access.2024.3386640

JYU authors or editors

Publication details

All authors or editors: Taipalmaa, Jussi; Raitoharju, Jenni; Queralta, Jorge Peña; Westerlund, Tomi; Gabbouj, Moncef

Journal or series: IEEE Access

eISSN: 2169-3536

Publication year: 2024

Volume: 12

Pages range: 52428-52438

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/access.2024.3386640

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/94522

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.

Keywords: water rescue; sea rescue; recognition; deep learning

Free keywords: search and rescue (SAR); person-in-water; unmanned aerial vehicle (UAV); object detection; deep learning (DL); dataset

Contributing organizations

Ministry reporting: Yes

Preliminary JUFO rating: 1