Implementation of Deep-Learning-based Edge Computing for Preventing Drowning

Abstract

With the increasing popularity of computer vision and ocean engineering, maritime visual surveillance has emerged as an important area of study. Despite the development of various detection or surveillance techniques for maritime locations, maritime visual surveillance remains a challenge owing to the complex, unconstrained, and diverse nature of such places. In addition, only a few studies have investigated edge computing in actively preventing people from drowning. Considering several people lose their lives due to drowning, the flourishing technologies of artificial intelligence (AI) and deep learning have been used for our study. In this paper, an implementation of deep-learning-based edge computing to prevent drowning with the use of NVIDIA Jetson Nano is proposed.