Abstract

Drowning is a leading cause of accidental fatalities worldwide, making timely intervention critical. This study analyzes underwater drowning detection using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) with transfer learning to improve accuracy. We evaluate five CNN architectures - MobileNetV3, EfficientNet, ResNet, AlexNet, and DenseNet121 - alongside ViTs to assess their effectiveness in detecting drowning incidents from underwater video footage. The models are trained and tested on a curated dataset with varying underwater conditions, including low visibility and lighting fluctuations. Performance is measured using accuracy, precision, recall, F1-score, and inference speed to identify the best model for real-time drowning detection. The results show that EfficientNet achieves the highest detection accuracy of 97% for swimming and 98% for drowning detection, outperforming other CNN models with a real-time inference time of 0.04 seconds. In contrast, ViTs demonstrate strong feature extraction capabilities but require higher computational resources with inference time of 0.12 seconds. Additionally, transfer learning significantly improves model generalization, reducing false alarms and enhancing response efficiency. This study highlights the potential of deep learning-based approaches for automated underwater drowning detection, providing a reliable solution for surveillance systems and rescue operations. Future work will focus on optimizing Vision Transformers (ViTs) for real-time deployment and integrating them with IoT-based alert systems to enhance the responsiveness and effectiveness of drowning detection solutions.