Implementation of Deep-Learning-based Edge Computing for Maritime Vehicle Classification

Abstract

In recent years, Artificial Intelligence (AI) has revolutionized almost every field – from automotive industry to electrical industry, continuously affecting the dynamics of society and the lives of consumers. Among the countless applications of AI, AI at the edge provides the ideal solution for specific workload acceleration. Despite the development of various techniques for surveillance in maritime scenarios, automatic maritime vehicle classification of visual surveillance remains a challenge owing to the small dataset, as well as complex, unconstrained, and diverse nature of such scenarios. To date, only a few studies have investigated edge computing in maritime vehicle classification with edge device. In this paper, data augmentation is applied to deal with the small dataset problem because of a lack of images of military ships. Furthermore, the implementation of deep-learning-based edge computing in maritime vehicle classification through the use of NVIDIA Jetson Nano is proposed.