GS-YOLO-Seg: A Lightweight Instance Segmentation Method for Low-Grade Graphite Ore Sorting Based on Improved YOLO11-Seg

Zeyang Qiu, Xueyu Huang (), Zhaojie Sun, Sifan Li and Jionghui Wang
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Zeyang Qiu: School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China
Xueyu Huang: School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China
Zhaojie Sun: School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China
Sifan Li: School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China
Jionghui Wang: Minmetals Exploration and Development Co., Ltd., Beijing 100010, China

Sustainability, 2025, vol. 17, issue 12, 1-21

Abstract: Efficient identification and removal of low-grade minerals during graphite ore processing is essential for improving product quality, optimizing resource recovery, and promoting sustainable production. To address the limitations of traditional sorting methods and performance bottlenecks in edge devices, this paper proposes a lightweight instance segmentation model, GS-YOLO-seg, for rapid identification and intelligent sorting of low-grade graphite ore in industrial production lines. The model first reduces network depth by adjusting the depth factor. Subsequently, the backbone network adopts the lightweight and efficient GSConv to perform downsampling, while a novel C3k2-Faster architecture is proposed to improve the effectiveness of feature extraction. Finally, the Segment-Efficient segmentation head is optimized to reduce redundant computations, further lowering the model load. On a self-constructed graphite ore image dataset, GS-YOLO-seg achieved comparable segmentation performance to the baseline YOLO11n-seg, while achieving a 30% reduction in FLOPs, 59% fewer parameters, 56% smaller model size, and 8% higher FPS. This method enhances the intelligence of the sorting process, preventing low-grade ores from entering subsequent stages, thus reducing resource waste, energy consumption, and carbon emissions, providing crucial technical support and feasible deployment pathways for building intelligent, green, and sustainable mining systems.

Keywords: sustainable production; green mining; instance segmentation; graphite ore; YOLO11-seg; lightweight network; edge deployment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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