Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means

TL;DR

This paper introduces dropout-based clustering algorithms to optimize marine buoy placement for ship detection, enhancing robustness against buoy disruptions and improving detection probabilities in simulated scenarios.

Contribution

It proposes dropout k-means and dropout k-median algorithms for robust buoy placement, a novel approach in marine vessel detection applications.

Findings

Dropout k-median achieves the highest detection probability of 52%.

Dropout methods outperform classic clustering in robustness.

Simulations based on AIS data validate improved detection rates.

Abstract

Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means and dropout k-median to improve placement robustness to buoy disruption. We simulated the passage of ships in the Gabonese waters near West Africa using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%.