# Leveraging synthetic imagery for collision-at-sea avoidance

## TL;DR

This paper introduces a collision avoidance system for ships using CNNs trained on synthetic maritime imagery, along with a large synthetic dataset, to improve maritime safety.

## Contribution

The paper presents a novel shipboard collision avoidance system utilizing synthetic imagery and introduces the NAVHAZ-Synthetic dataset for training and testing.

## Key findings

- Promising performance of the vision-based warning system.
- Effective detection of vessels under various sea conditions.
- Demonstrated utility of synthetic data for maritime collision avoidance.

## 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.