Roof bolts such as rock bolts and cable bolts provide structural support in underground mines. Frequent assessment of these support structures is critical to maintain roof stability and minimise safety risks in underground environments. This study proposes a robust workflow to classify roof bolts in 3D point cloud data and to generate maps of roof bolt density and spacing. The workflow was evaluated for identifying roof bolts in an underground coal mine with suboptimal lighting and global navigation satellite system (GNSS) signals not available. The approach is based on supervised classification using the multi-scale Canupo classifier coupled with a random sample consensus (RANSAC) shape detection algorithm to provide robust roof bolt identification. The issue of sparseness in point cloud data has been addressed through upsampling by using a moving least squares method. The accuracy of roof bolt identification was measured by correct identification of roof bolts (true positives), unidentified roof bolts (false negatives), and falsely identified roof bolts (false positives) using correctness, completeness, and quality metrics. The proposed workflow achieved correct identification of 89.27% of the roof bolts present in the test area. However, considering the false positives and false negatives, the overall quality metric was reduced to 78.54%.

诸如岩石螺栓和电缆螺栓之类的屋顶螺栓为地下矿井提供了结构支撑。经常对这些支撑结构进行评估对于保持屋顶稳定性并最大程度降低地下环境中的安全风险至关重要。这项研究提出了一个强大的工作流程，可以在3D点云数据中对屋顶螺栓进行分类，并生成屋顶螺栓密度和间距的地图。对工作流程进行了评估，以识别地下煤矿中的屋面螺栓，该处的照明欠佳，并且全球导航卫星系统（GNSS）信号不可用。该方法基于监督分类，该监督分类使用多尺度Canupo分类器结合随机样本共识（RANSAC）形状检测算法来提供可靠的屋顶螺栓识别。点云数据稀疏的问题已通过使用移动最小二乘法进行升采样得到解决。屋顶螺栓识别的准确性是通过使用正确性，完整性和质量指标正确识别屋顶螺栓（真阳性），未识别屋顶螺栓（假阴性）和错误识别屋顶螺栓（假阳性）来衡量的。提议的工作流程可以正确识别测试区域中存在的89.27％的屋顶螺栓。但是，考虑到误报和误报，整体质量指标降低到78.54％。完整性和质量指标。提议的工作流程可以正确识别测试区域中存在的89.27％的屋顶螺栓。但是，考虑到误报和误报，整体质量指标降低到78.54％。完整性和质量指标。提议的工作流程可以正确识别测试区域中存在的89.27％的屋顶螺栓。但是，考虑到误报和误报，整体质量指标降低到78.54％。