模块CLIP:用于预测多模式机器人系统中的基础坑变形的多尺度CLIP框架.
Lin Wenbo1, Li Tingting2, Li Xiao3
1School of Geology, Gansu Industrial Vocational and Technical College, Tianshui, Gansu, China.
Frontiers in neurorobotics
|April 16, 2025
概括
一个新的ModuCLIP框架使用多模式数据准确地预测了基础坑变形. 这种方法通过提高预测准确性和稳定性来提高地下工程的安全性.
科学领域:
- 地质技术工程 地质技术工程
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 基础坑变形预测对于地下工程安全至关重要.
- 传统方法面临复杂的地质条件和多模式数据集成的挑战.
- 深度学习,特别是交叉模式架构,是有前途的,但需要有效的数据融合.
研究的目的:
- 提出ModuCLIP,一个用于基础坑变形预测的多尺度对比语言图像预训框架.
- 通过整合多源数据 (图像,文本,传感器数据) 来提高预测的准确性和稳定性.
- 通过使用多模式机器人系统,提高对复杂工程条件的适应能力.
主要方法:
- 开发了一种名为ModuCLIP的多尺度对比语言图像训练 (CLP) 框架.
- 采用自主监督的对比学习机制,用于多源信息集成.
- 使用多级特征学习方法来提高适应能力.
主要成果:
- 与现有方法相比,ModuCLIP显示出更高的预测准确度.
- 该框架在多个基础坑数据集中显示出更好的概括性和稳定性.
- 实验结果验证了多模式数据集成的有效性.
结论:
- ModuCLIP为基础坑变形预测提供了一种高效和精确的解决方案.
- 该研究提供了对工程监控多模式机器人感知的见解.
- 这一框架促进了深度学习在地下工程安全方面的应用.
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