DisBeaNet: A Deep Neural Network to augment Unmanned Surface Vessels for maritime situational awareness
This addresses the vulnerability of USVs to detection in contested environments by providing a passive sensing alternative, though it is incremental as it applies existing deep learning to a new domain-specific application.
The paper tackles the problem of maritime situational awareness for unmanned surface vessels by proposing a low-cost vision perception system that detects, tracks, and estimates distance and bearing of vessels using a monocular camera, enabling latitude and longitude determination without relying on radar or AIS.
Intelligent detection and tracking of the vessels on the sea play a significant role in conducting traffic avoidance in unmanned surface vessels(USV). Current traffic avoidance software relies mainly on Automated Identification System (AIS) and radar to track other vessels to avoid collisions and acts as a typical perception system to detect targets. However, in a contested environment, emitting radar energy also presents the vulnerability to detection by adversaries. Deactivating these Radiofrequency transmitting sources will increase the threat of detection and degrade the USV's ability to monitor shipping traffic in the vicinity. Therefore, an intelligent visual perception system based on an onboard camera with passive sensing capabilities that aims to assist USV in addressing this problem is presented in this paper. This paper will present a novel low-cost vision perception system for detecting and tracking vessels in the maritime environment. This novel low-cost vision perception system is introduced using the deep learning framework. A neural network, DisBeaNet, can detect vessels, track, and estimate the vessel's distance and bearing from the monocular camera. The outputs obtained from this neural network are used to determine the latitude and longitude of the identified vessel.