LGG-YOLO-Seg: A Lightweight Real-Time Instance Segmentation Framework for Low-Grade Graphite Ore Sorting

Real-time instance segmentation of low-grade graphite ores is essential for intelligent mineral-sorting systems, yet existing methods often struggle to balance segmentation accuracy, computational efficiency, and lightweight deployment under irregular boundaries, multi-scale targets, and complex illumination conditions. To address these challenges, this paper proposes LGG-YOLO-Seg, a lightweight and efficient instance segmentation framework based on YOLO11-seg. First, a compact baseline is constructed through depth scaling to reduce redundant computation and model complexity. Then, Linear Deformable Convolution (LDConv) is embedded into the backbone to enhance adaptive feature extraction for irregular ore boundaries. Meanwhile, the feature extraction module is redesigned as C3k2-PKI using multi-kernel and depthwise separable convolutions to improve multi-scale representation capability. In addition, a lightweight Segment-EfficientHead is introduced to reduce computational overhead while maintaining segmentation accuracy. Experiments conducted on a self-constructed graphite ore dataset demonstrate that LGG-YOLO-Seg improves mAP50box and mAP50mask by 0.9% and 0.4%, respectively, compared with YOLO11n-seg while reducing FLOPs, parameters, and model size by 28.8%, 55.6%, and 55.1%, respectively, with an 11.0% increase in inference speed, showing strong potential for real-time industrial sorting and edge deployment.