Aerial Maritime Vessel Detection and Identification

TL;DR

This paper presents an autonomous UAV system for maritime vessel detection and identification in GNSS-denied environments, combining deep learning detection with feature matching for target localization.

Contribution

It introduces a novel method integrating YOLOv8 detection with feature matching and hue analysis for vessel identification without GNSS, validated in real-world experiments.

Findings

Effective vessel detection using YOLOv8 in complex maritime scenes

Successful target identification through feature matching and hue analysis

Demonstrated system operates autonomously in GNSS-denied conditions

Abstract

Autonomous maritime surveillance and target vessel identification in environments where Global Navigation Satellite Systems (GNSS) are not available is critical for a number of applications such as search and rescue and threat detection. When the target vessel is only described by visual cues and its last known position is not available, unmanned aerial vehicles (UAVs) must rely solely on on-board vision to scan a large search area under strict computational constraints. To address this challenge, we leverage the YOLOv8 object detection model to detect all vessels in the field of view. We then apply feature matching and hue histogram distance analysis to determine whether any detected vessel corresponds to the target. When found, we localize the target using simple geometric principles. We demonstrate the proposed method in real-world experiments during the MBZIRC2023 competition, integrated into a fully autonomous system with GNSS-denied navigation. We also evaluate the impact of perspective on detection accuracy and localization precision and compare it with the oracle approach.