DisBeaNet：一种用于增强无人水面舰艇海上态势感知能力的深度神经网络
DisBeaNet: A Deep Neural Network to augment Unmanned Surface Vessels for maritime situational awareness
AI总结：
针对无人水面舰艇雷达等主动感知设备易暴露的问题，提出基于深度学习的低成本视觉感知系统DisBeaNet，可通过单目摄像头实现船舶检测跟踪及方位距离估算，辅助提升海上态势感知能力。
摘要：
海上船舶的智能检测与跟踪对无人水面舰艇（USV）执行交通避碰具有重要作用。当前的交通避碰软件主要依靠自动识别系统（AIS）和雷达跟踪其他船舶以避免碰撞，作为典型的感知系统完成目标检测任务。然而在对抗环境中，发射雷达能量也会带来被敌方探测到的脆弱性。关闭这些射频发射源会提升被探测的威胁，同时削弱USV监控附近航运交通的能力。因此，本文提出了一种基于船载摄像头、具备被动感知能力的智能视觉感知系统，旨在协助USV解决这一问题。本文将提出一种用于海事环境下船舶检测与跟踪的新型低成本视觉感知系统，该系统基于深度学习框架构建。其中的DisBeaNet神经网络可通过单目摄像头实现船舶检测、跟踪，并估算船舶的距离与方位，该神经网络的输出可用于确定被识别船舶的经纬度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
