MUSeg:用于复杂的地下矿场景的多模式语义细分数据集
Shiyan Li1, Qingqun Kong1, Xuan Gao1
1School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing, 100083, China.
Scientific data
|July 8, 2025
概括
这项研究介绍了MUSeg,这是一个新的多式联网数据集,用于智能采矿感知. 它解决了地下矿山视觉感知方面的挑战,使先进的多式联接融合语义细分成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 地质科学工程 工程地质科学工程
背景情况:
- 由于恶劣的环境,地下采矿面临视觉感知方面的挑战,限制了智能自动化.
- 在这些条件下,基于可见光的方法很难实现,因此需要采用多式联络方法.
- 现有的多式联运数据集对于复杂的地下矿场景是不够的.
研究的目的:
- 开发和引入多模式地下矿山语义细分 (MUSeg) 数据集.
- 为地下矿山中多式融合语义细分提供一个基准数据集.
- 促进采矿领域智能感知技术的研究和应用.
主要方法:
- 从六个不同的中国矿山收集了3,171个对齐的RGB和深度图像对.
- 手动注释了15个语义对象类别,与我的感知任务相关.
- 与采矿专家验证了注释,并使用经典算法评估了数据集.
主要成果:
- MUSeg数据集为地下矿山中的多式模式语义细分提供了一个全面的资源.
- 它解决了在这个专业领域中缺少专用数据集的严重问题.
- 最初的评估证明了数据集对基准测试算法的实用性.
结论:
- MUSeg数据集对智能采矿做出了重大贡献,可以在感知方面取得进步.
- 它为开发和应用强大的多式联络融合算法提供了基础.
- 这一资源将加速向无人和智能采矿业务的过渡.
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