@inproceedings{d4029b938ff9448a88b75b790e841d5a,
title = "Multimodal ship detection using YOLOv11: Comparative analysis of SWIR, LWIR, and visible sensors under diverse maritime and environmental conditions",
abstract = "Reliable ship detection is a critical capability for maritime security and military operations, particularly in scenarios where vessels do not broadcast Automatic Identification System (AIS) signals. In this paper, we investigate the use of computer vision techniques for automatic ship detection using a multimodal sensor suite comprising Short-Wave Infrared (SWIR), Long-Wave Infrared (LWIR), and visible spectrum cameras. We first conduct a qualitative analysis of the strengths and weaknesses of each sensor across varying environmental conditions using a custom dataset acquired in diverse atmospheric scenarios and against a range of backgrounds and ship types. Subsequently, we perform a quantitative evaluation by training and testing YOLOv5 and YOLOv11 models on data from each sensor type and comparing detection performance across video sequences captured in representative maritime conditions.",
keywords = "CNN, deep neural network, maritime surveillance, ship detection, thermal infrared imaging, YOLO",
author = "Simon Rennotte and Cornelia Nita and Demeyer, \{Pieter Jan\} and Marijke Vandewal",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 3rd Artificial Intelligence for Security and Defence Applications ; Conference date: 16-09-2025 Through 18-09-2025",
year = "2025",
month = oct,
day = "28",
doi = "10.1117/12.3069996",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "Society of Photo-Optical Instrumentation Engineers",
editor = "Kuijf, \{Hugo J.\} and Radhakrishna Prabhu and Yitzhak Yitzhaky",
booktitle = "Artificial Intelligence for Security and Defence Applications III",
}