AI Powered Drowning Detection and Alert System for Swimming Pool

Abstract: The use of Artificial Intelligence in intelligent surveillance systems has improved the ability to detect emergency situations automatically and efficiently. This project presents an AI Powered Human Drowning Detection and Alert System designed for swimming pool safety using Computer Vision and Deep Learning techniques. The proposed system continuously monitors uploaded media files and live webcam streams to identify possible drowning situations in real time.

The system is implemented using a custom-trained YOLOv8 model integrated with OpenCV for image and video processing. Flask is used for backend processing, while HTML, JavaScript, and Tailwind CSS are used to develop the frontend monitoring interface. The system performs frame-by-frame analysis to detect swimmers, generate bounding boxes, and classify activities based on prediction confidence.

To improve detection reliability, the system applies confidence thresholding, frame buffering, and danger percentage analysis to reduce false alerts caused by temporary movements or water disturbances. Whenever dangerous activity is detected continuously, the system immediately generates warning notifications and audio alerts.

The developed solution provides a practical, affordable, and real-time safety monitoring system for swimming pools and other aquatic environments. By combining deep learning, intelligent surveillance, and automated alert generation, the system helps improve swimmer safety and reduces emergency response time.

Keywords: Artificial Intelligence, YOLOv8, Drowning Detection, Computer Vision, OpenCV, Deep Learning, Real- Time Monitoring, Surveillance System, Swimming Pool Safety, Alert System.