Intelligent video surveillance for early drowning detection using deep learning

Drowning is one of the leading causes of unintentional injury-related deaths worldwide, particularly among children. Lifeguards often fail to detect drowning in their early stages, leading to delayed interventions. To address this critical issue, we propose an intelligent video surveillance system leveraging deep learning techniques for early drowning detection. The system is designed to automatically monitor swimming pools, sea and rivers and other aquatic environments in real-time, identifying potential drowning incidents before they escalate. Our approach utilizes real-time object detection models, trained, and validated using a dataset composed of diverse swimming scenarios. We enhance the dataset with various augmentation techniques to improve the model’s robustness in different lighting conditions, camera angles, and environmental variations. Key performance metrics, including mean Average Precision (mAP), Recall, Precision, and F1- score, are employed to evaluate the effectiveness of the proposed solution. The model is deployed on edge devices such as NVIDIA Jetson Nano and Jetson Xavier, offering high computational efficiency while maintaining real-time detection capabilities. Through extensive experiments, the system achieved promising performance metrics, with a high mAP and F1-score, confirming its potential for reliable early drowning detection. This research presents a cost-effective, scalable solution aimed at improving the safety of aquatic environments, reducing the risk of drowning incidents, and facilitating timely interventions. By deploying on low-power edge platforms, the proposed system ensures accessibility and efficiency, contributing to enhanced public safety.