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Leveraging synthetic imagery for collision-at-sea avoidance
Author(s): Chris M. Ward; Josh Harguess; Alexander G. Corelli
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Paper Abstract

Maritime collisions involving multiple ships are considered rare, but in 2017 several United States Navy vessels were involved in fatal at-sea collisions that resulted in the death of seventeen American Servicemembers. The experimentation introduced in this paper is a direct response to these incidents. We propose a shipboard Collision-At-Sea avoidance system, based on video image processing, that will help ensure the safe stationing and navigation of maritime vessels. Our system leverages a convolutional neural network trained on synthetic maritime imagery in order to detect nearby vessels within a scene, perform heading analysis of detected vessels, and provide an alert in the presence of an inbound vessel. Additionally, we present the Navigational Hazards - Synthetic (NAVHAZ-Synthetic) dataset. This dataset, is comprised of one million annotated images of ten vessel classes observed from virtual vessel-mounted cameras, as well as a human “Topside Lookout” perspective. NAVHAZ-Synthetic includes imagery displaying varying sea-states, lighting conditions, and optical degradations such as fog, sea-spray, and salt-accumulation. We present our results on the use of synthetic imagery in a computer vision based collision-at-sea warning system with promising performance.

Paper Details

Date Published: 4 May 2018
PDF: 8 pages
Proc. SPIE 10645, Geospatial Informatics, Motion Imagery, and Network Analytics VIII, 1064507 (4 May 2018); doi: 10.1117/12.2306113
Show Author Affiliations
Chris M. Ward, SPAWAR Systems Ctr. Pacific (United States)
Josh Harguess, SPAWAR Systems Ctr. Pacific (United States)
Alexander G. Corelli, SPAWAR Systems Ctr. Pacific (United States)

Published in SPIE Proceedings Vol. 10645:
Geospatial Informatics, Motion Imagery, and Network Analytics VIII
Kannappan Palaniappan; Peter J. Doucette; Gunasekaran Seetharaman, Editor(s)

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