  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.
