Weakly supervised semantic segmentation of 3D point clouds for coal mine roadways: Method and application
With advantages in anti-interference and active sensing, Light Detection and Ranging (LiDAR) has become a primary modality for environmental perception in underground coal mines characterized by inadequate illumination and high dust concentrations. However, raw point clouds lack semantic attributes, which limits their direct application in scene understanding for digital mines. Although fully supervised deep learning methods for point cloud semantic segmentation achieve high performance, they rely heavily on labor-intensive point-wise annotations, incurring high costs and potential human error. To address this, this paper proposes a weakly supervised semantic segmentation framework specifically designed for coal mine roadways. The framework establishes a comprehensive workflow spanning from data acquisition to sparse-supervised training. By leveraging sparse labels to drive model optimization, the method maintains segmentation performance while significantly reducing annotation costs, achieving a strategic balance between engineering precision and deployment overhead. Experimental results demonstrate that using only 0.1% sparse annotations, the proposed method achieves an Overall Accuracy (OA) of 91.64% and a mean Intersection over Union (mIoU) of 78.83%. Compared to fully supervised models, it achieves a 2-3 times improvement in annotation efficiency with a precision loss of approximately 7%. The segmented structural point clouds are utilized for roadway deformation monitoring and parametric geometric reconstruction. This supports a comprehensive digital twin framework that achieves high-fidelity modeling and centimeter-level deformation early-warning. This research confirms that the proposed technology provides a cost-effective and efficient path for scene understanding in intelligent mines, demonstrating substantial engineering value and potential for industrial promotion.