一个深度学习的仿生银河系指南针
Yiting Tao1, Michael Lucas1, Asanka Perera2
1School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了一种基于视觉的方法,使用银河系 (MW) 进行昆虫导航. 该算法可靠地检测方向变化,这对于自动驾驶系统非常有用.
科学领域:
- 天文学 天文学
- 伦理学 伦理学 伦理学
- 计算机视觉 计算机视觉
背景情况:
- 生物为了生存而直线移动,利用环境线索进行导航.
- 夜间活跃的昆虫使用天体线索,包括银河系 (MW),以求方向.
- 传统的图像处理方法在具有挑战性的照明条件下难以对MW进行细分.
研究的目的:
- 开发和评估一种基于视觉的算法,用于使用MW检测航线方向变化.
- 评估MW导向对自主导航系统的可靠性.
- 为了克服现有的MW细分技术在不同光线条件下的局限性.
主要方法:
- 一个新的算法结合YOLOv8m-seg和正常化的第二中央时刻来计算MW方向.
- 训练用于YOLOv8m-seg模型的MW图像的定制数据集.
- 在南澳大利亚的实地试验中,将细分的MW图像与GPS定向数据进行比较.
主要成果:
- 在验证数据集上,YOLOv8m-seg模型实现了84.7%的分段化mAP@0.5.
- 分段的MW图像被证明是短期导航的可靠方向提示.
- 在旋转过程中,在拟议的方法和地面真相之间观察到一个小的角度差异 (5-10°).
结论:
- 拟议的算法提供了一种强大的方法来检测MW的方向,即使有显著的月光或光污染.
- 这种技术显示出增强自主导航系统的巨大潜力,特别是对于夜间活动的昆虫.
- 该研究验证了MW作为人工系统在现实世界条件下可行的导航辅助器.
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