ENJAD - A Deep Learning-Based Drowning Detection System

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.