A Lightweight Machine Vision-Based Instance Segmentation Algorithm for Low-Grade Graphite Ore Sorting

Accurate identification of low-grade graphite ore is important for improving resource utilization and intelligent mineral sorting. To reduce the computational burden of existing instance segmentation models, this study proposes a lightweight model, RVE-YOLO-seg, based on YOLOv12-seg. GhostConv is introduced for lightweight downsampling, C3k2-RVE is designed to enhance fine-grained feature representation, and Segment-SEAM is employed to strengthen mask-oriented feature extraction. Experiments on a self-constructed dataset of 1978 conveyor-belt images and 19,614 annotated ore instances show that RVE-YOLO-seg achieves 92.7% mAP50 for bounding boxes and 86.3% mAP50 for masks, comparable to YOLOv12n-seg. Meanwhile, the parameter count, FLOPs, and model size are reduced by 56.7%, 24.6%, and 52.5%, respectively, with an inference speed of 80 FPS. Multi-seed experiments and retraining on an external mineral-image dataset further demonstrate stable performance and cross-dataset applicability. These results indicate that RVE-YOLO-seg achieves a favorable accuracy–efficiency trade-off for resource-constrained graphite ore sorting.