Preliminary study for developing a vision-based detection system of unmanned surface vessels

Abstract

This paper describes the framework for a vision-based detection system of unmanned surface vessels (USVs) with minimizing human intervention. In order to compensate each other's drawbacks due to characteristics of each camera, the system consists of an electro-optical (EO) camera, an infrared (IR) camera, and panorama cameras for omnidirectional situational awareness. For object identification and recognition, a systematic procedure using image processing and convolutional neural network (CNN) approaches is framed. As preliminary study, the performance and practical feasibility of the proposed approach demonstrate using field test dataset.