Sanxi Li
Papers
1
Total Citations
60
H-Index
1
About
Sanxi Li is a researcher advancing intelligent automation in the mining and mineral processing industries, with a primary focus on computer vision and deep learning for resource sorting. His most notable contribution is the development of a lightweight YOLO-based coal gangue detection algorithm, which integrates a ResNet18 backbone feature network to enable real-time, efficient identification of coal and waste rock. This work directly addresses the longstanding challenges of manual presorting—high labor intensity, low efficiency, and safety risks—by providing a robust, deployable solution for coal gangue sorting robots. With 60 citations since 2023, this paper has quickly gained traction as a foundational reference for lightweight object detection in industrial settings. Li’s research bridges the gap between state-of-the-art neural network architectures and practical engineering constraints, making automated sorting more accessible and reliable. His work is particularly impactful for researchers and engineers seeking energy-efficient, high-speed vision systems for harsh environments, and it underscores a broader trend toward intelligent, human-free operations in the mining sector.
Research Focus
Key Achievements
Top Papers
- 1