Abstract
Spatial interference among the shearer cutting drum, ranging arm, and hydraulic-support face guard is a practical safety risk in fully mechanized coal mining faces, where dust, water mist, weak illumination, and metal multipath reflections limit vision- and LiDAR-based perception. This study develops a 4D millimeter-wave radar point cloud sensing method for face guard segmentation and cutting-interference warning. Radar point clouds were extracted from ROS bag data, converted to frame-level point cloud files, manually labeled, and processed using a joint passthrough-radius filtering strategy. A PointNet++ semantic segmentation network was modified with radar feature augmentation and class-aware neighborhood sampling to identify sparse face guard points from background structures. The segmented face guard points were fitted by an axis-aligned 3D bounding box, and the minimum distance from shearer dangerous regions to the box was used to classify safe, warning, and dangerous states. On a 2160-frame labeled dataset, the final segmentation model reached 99.126% precision, 97.477% recall, and 96.608% IoU at epoch 100. Static pose, dust-condition, and dynamic sequence tests further verified that the proposed sensing pipeline can provide continuous face guard localization and distance-based interference monitoring under representative experimental conditions.