<b>MUSe</b><b>g</b><b>:</b><b>A multimodal semantic segmentation dataset for complex underground mine scenes</b>

The MUSeg dataset comprises 3,171 precisely aligned RGB and depth image pairs collected from six typical mines across different regions of China. According to the requirements of mine perception tasks, we manually annotated 15 categories of semantic objects, with all labels reviewed and verified by mining experts. The dataset has also been evaluated using classical multimodal semantic segmentation algorithms. MUSeg not only fills the gap in this field but also provides a critical foundation for research and application of multimodal perception algorithms in mining, contributing significantly to the advancement of intelligent mining.MUSeg数据集（MUSeg）包含3171对经过精确对齐的RGB图像与深度图像，数据采集自中国不同地区的六座典型矿山。根据矿山感知任务的相关要求，研究团队对15类语义目标进行了人工标注，所有标注标签均经过采矿专家的审核与验证。该数据集还采用经典多模态语义分割算法进行了性能评估。MUSeg数据集不仅填补了该领域的研究空白，还为多模态感知算法在矿山场景中的研究与应用提供了关键基础，对智能采矿技术的发展具有显著推动作用。

搜集汇总

背景与挑战

背景概述

MUSeg是一个针对复杂地下矿山场景的多模态语义分割数据集，包含3,171个精确对齐的RGB-D图像对，标注了15个语义类别，并由专家验证。该数据集填补了该领域空白，为采矿环境中的多模态感知算法研究提供了重要资源。