IceHorizon: A Dataset for Horizon Detection in Ice-Covered Maritime Environments and Comparative Evaluation of Detection Methods

2026-08-07

AI Summary

This study addresses the challenges of horizon detection in icy maritime imagery, where low sea-sky contrast, ice clutter, and varying illumination conditions degrade performance. To this end, the authors introduce IceHorizon, the first dataset specifically designed for this scenario, comprising 30 shipborne and 8 UAV video sequences. They systematically evaluate six detection methods, including four classical computer vision algorithms and two hybrid approaches that integrate deep learning with traditional line-detection techniques. Experimental results demonstrate that the proposed hybrid methods achieve superior accuracy, robustness, and computational efficiency, with shipborne platforms significantly outperforming UAV-based ones. The dataset and source code have been publicly released to support future research in this domain.

Abstract

Horizon detection in images of ice-covered waters is a challenging problem for maritime navigation due to low contrast between water and sky, cluttered ice structures, and varying illumination conditions. This paper presents a comparative evaluation of six horizon detection algorithms, including four classical computer vision methods and two hybrid approaches combining deep learning with classical line detection. A new bespoke IceHorizon dataset consisting of 30 ship-based and 8 drone-based videos is used to evaluate detection accuracy, horizon coverage, and computational performance. The results show that hybrid methods achieve the highest accuracy and most reliable horizon estimates. In contrast, purely classical methods exhibit reduced robustness, particularly in visually ambiguous scenes. Performance on ship-based imagery was consistently higher than on drone-based imagery, indicating a strong dependency on acquisition characteristics. The created dataset and codes used in this study are made publicly available to support further research on this topic. The code is available at https://github.com/allythe/HorizonDetection. The dataset is available at https://doi.org/10.5281/zenodo.20411867

Problem

• horizon detection
• ice-covered waters
• maritime navigation
• low contrast
• cluttered ice structures

Innovation

• horizon detection
• ice-covered maritime environments
• hybrid deep learning
• IceHorizon dataset
• comparative evaluation