ENJAD - A Deep Learning-Based Drowning Detection System
Year: 2025
Type: article
Abstract: Saudi Arabia reported 275 drowning incidents from 2023 to July, with Makkah having the highest with 95 incidents. Hence, introducing a drowning detection system is crucial in addressing this issue. The system we developed focuses on monitoring swimmers' physical behaviour to detect sudden irregularities and alert adults in real-time. It incorporates low-light image enhancement technology to enhance visual clarity, aiding in accurate motion recognition and improving drowning detection. This solution can enhance safety measures and prevent drowning incidents, offering a proactive approach to safeguarding individuals engaged in water-related activities.
Authors: Eidaroos, Shefaa, Alzahrani, Bayan, Alshehri, Sarah, Saeed, Sara
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7091395078
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