对比六种海冰海域地平线检测方法，混合模型表现最佳。
IceHorizon: A Dataset for Horizon Detection in Ice-Covered Maritime Environments and Comparative Evaluation of Detection Methods
- 融合深度学习与传统直线检测的混合方法
- 混合模型在准确率和稳定性上均领先
- 适合自动驾驶、航海导航等场景研究者
海冰覆盖水域的地平线检测因水天低对比度、冰面杂乱结构及光照变化而极具挑战。本文对六种地平线检测算法进行对比评估，包括四种经典计算机视觉方法和两种结合深度学习与经典线检测的混合方法。使用自建的IceHorizon数据集（30段船载视频 + 8段无人机视频）评估检测精度、地平线覆盖范围及计算性能。结果表明，混合方法在准确性和鲁棒性上表现最优；纯经典方法在视觉模糊场景中效果显著下降。船载图像性能普遍优于无人机图像，凸显采集条件的影响。本研究代码与数据集已公开，支持后续研究。
原文摘要 · Abstract (English)
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
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