一个扩展的数据库注释天窗偏振图像,涵盖两个月的时间
Léo Poughon1, Vincent Aubry2, Jocelyn Monnoyer2
1Aix Marseille Univ, CNRS, ISM, Marseille, France. leo.poughon@univ-amu.fr.
BMC research notes
|October 14, 2024
概括
研究人员创建了一个新的数据集真实世界的天空偏振图像的生物灵感导航. 这一数据集通过在各种天气条件中提供注释的极度测量数据来帮助机器学习.
科学领域:
- 光学和光子学 在光学和光子学.
- 机器人和导航 机器人和导航
- 计算机视觉 计算机视觉
背景情况:
- 生物启发的导航利用自然现象,如天窗两极分化,用于定向.
- 导航的机器学习需要广泛的,注释的数据集,目前缺乏用于现实世界的天空两极分化.
- 现有的数据集主要由模拟图像组成,限制了强大的导航系统的开发.
研究的目的:
- 为了解决现实世界注释天空极化数据的稀缺性.
- 为培训和评估生物启发导航中的机器学习算法提供一个有价值的资源.
- 推进基于天窗的定向传感器的发展.
主要方法:
- 一个长期的实验设置被用来收集天空的极度测量图像.
- 使用了一个带有鱼眼镜头和旋转望远镜支架的焦点平面偏振摄像头.
- 通过几何校准和提供算法来确定东北向上 (ENU) 框架中的传感器定位.
主要成果:
- 创建了一个公开可用的数据集,包括两个月的现实世界色彩极度测量天空图像.
- 图像以10分钟的间隔拍摄,曝光时间不同 (33μs到300ms).
- 该数据集包括对深度学习应用程序至关重要的手册注释.
结论:
- 发布的数据集填补了生物灵感导航和天窗两极化研究的关键空白.
- 该资源将加速先进导航系统的开发和验证.
- 方便对天空极化用于定向的实际应用进行进一步的研究.
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