Citation

P D, Durairaj M, Subudhi S, Rao VVRM, Jayanthi J, Suganthi D. Spat. Inf. Res. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Korean Spatial Information Society, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s41324-023-00549-7

PMID

unavailable

Abstract

Recent observations indicate that nearly 50% of the public frequently visit coastal areas during weekends, seeking the health benefits of natural sunlight and fostering familial bonds. Notably, a significant portion of these visitors are unaware of swimming techniques or face other physical challenges, rendering them vulnerable to drowning, especially in areas lacking adequate lifeguard support or immediate medical emergency services. This study introduces an advanced drowned-detection device that employs a deep learning algorithm, grounded in artificial intelligence architecture, to swiftly detect and address potential drowning incidents. The system is particularly vigilant towards high-risk groups, such as children and the elderly. Upon detecting a threat, it autonomously deploys drones equipped with inflatable rescue tubes and notifies local authorities. Preliminary results suggest that our proposed model can effectively rescue a drowning individual in under 7 min, highlighting its prospective utility in curtailing swimming-related fatalities worldwide. This research underscores the need for technological intervention to enhance safety measures at coastal destinations and seeks to raise awareness about the importance of well-established lifeguard support.

Language: en

Keywords

Autonomous drones; Coastal safety; Deep learning algorithm; Drowned-detection; Swimming-related fatalities