发表机构
Mid Sweden University（中瑞典大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文构建了IceHorizon数据集，对比评估六种地平线检测算法，发现混合方法准确率最高，且船舶图像检测性能优于无人机图像，相关资源已公开供后续研究使用。
AI 中文摘要
冰盖水域图像中的地平线检测对海上导航而言是一项极具挑战性的问题，原因在于水与天空之间的对比度低、冰结构杂乱以及光照条件多变。本文对六种地平线检测算法展开对比评估，其中包含四种经典计算机视觉方法和两种结合深度学习与经典直线检测的混合方法。采用全新定制的IceHorizon数据集（由30个基于船舶的视频和8个基于无人机的视频构成），对检测准确率、地平线覆盖率及计算性能进行评估。结果表明，混合方法实现了最高的准确率和最可靠的地平线估计；相比之下，纯经典方法的鲁棒性有所降低，尤其在视觉模糊场景中表现明显。基于船舶图像的性能始终高于基于无人机图像的性能，这表明检测性能对采集特征存在较强的依赖性。本研究创建的数据集和使用的代码已公开，以支持该主题的进一步研究，代码可通过此https URL获取，数据集可通过此https URL获取。
英文摘要
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
