Summary of Disbeanet: a Deep Neural Network to Augment Unmanned Surface Vessels For Maritime Situational Awareness, by Srikanth Vemula et al.

DisBeaNet: A Deep Neural Network to augment Unmanned Surface Vessels for maritime situational awareness

by Srikanth Vemula, Eulises Franco, Michael Frye

First submitted to arxiv on: 10 May 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed intelligent visual perception system for unmanned surface vessels (USV) aims to address the limitations of current traffic avoidance software, which relies heavily on Automated Identification System (AIS) and radar. By utilizing an onboard camera with passive sensing capabilities, this system can detect and track vessels in a maritime environment without emitting radiofrequency energy, reducing the risk of detection by adversaries. The system employs a deep learning framework, specifically a neural network called DisBeaNet, which can detect vessels, track them, and estimate their distance and bearing from the monocular camera. This information is used to determine the latitude and longitude of the identified vessel.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The intelligent visual perception system for USV aims to help avoid collisions by detecting and tracking vessels at sea. The current method uses radar and AIS but emits energy that can be detected, making it vulnerable in a contested environment. To solve this problem, the system uses an onboard camera with passive sensing capabilities. A deep learning framework called DisBeaNet helps detect vessels, track them, and estimate their distance and bearing from the camera. This information is used to determine the location of the vessel.