Artificial Intelligence and Internet of Things Enabled Sea Water Life Saver System

Drowning remains a leading cause of unintentional deaths, particularly in unsupervised areas like pools and beaches. Traditional surveillance systems depend on human monitoring, which is prone to fatigue and delayed responses. To address this, we propose an AIbased drowning detection and alert system integrating IoT, computer vision, and real-time communication. The system uses a camera to capture live video, analyzed by a YOLOv3 deep learning model trained to detect abnormal swimming behaviors. Upon detecting potential drowning, it triggers a buzzer through an ESP32 microcontroller and sends SMS alerts to emergency contacts using the Twilio API. The MQTT protocol ensures low-latency communication between the AI model and IoT hardware. Developed using Python on Thonny IDE and integrated with MicroPython, this cost-effective, scalable system offers real-time monitoring and automated alerts. It reduces reliance on manual observation and enhances safety in public and private aquatic facilities.