基于图像的月球危险检测在低照明模拟条件下通过视觉变压器
Luca Ghilardi1, Roberto Furfaro1
1University of Arizona, Tucson, AZ 85721, USA.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
一种新的视觉变压器 (ViT) 模型在低光图像中有效检测月球着陆的危险. 这种深度学习方法的性能优于传统方法,提高了自主导航的安全性.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 太空探索 太空探索
背景情况:
- 自主登月需要强大的危险检测系统.
- 目前使用二维图像和三维激光雷达的方法在月球南极的低光条件下面临挑战.
- 阴影掩盖了关键的地形特征,阻碍了基于视觉的导航.
研究的目的:
- 开发和评估视觉变压器 (ViT) 模型,从低光月球图像中提取地形特征.
- 为了提高对月球任务的自主危险检测能力.
- 解决现有的基于视觉的系统在具有挑战性的照明中的局限性.
主要方法:
- 利用深度学习架构,视觉转换器 (ViT),以自然语言处理为灵感.
- 训练ViT模型分析低光RGB图像以提取地形特征.
- 将ViT的性能与UNet卷积神经网络进行比较.
主要成果:
- ViT模型在从低光图像中提取地形信息方面表现出强的表现.
- 性能在更高的海拔上特别有效.
- 在所有测试的场景中,ViT模型的表现优于UNet模型.
结论:
- 视觉变压器 (ViT) 模型为在具有挑战性的低光环境中自主探测月球危险提供了一个有前途的解决方案.
- 基于ViT的系统可以提高登月的安全性和可靠性.
- 这种深度学习方法超越了像UNet这样的传统方法,用于这个特定的应用.
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