Xianguo Li
Papers
2
Total Citations
50
H-Index
2
About
Xianguo Li is a leading researcher in intelligent mining and industrial automation, with a focus on deep learning and robotics for coal mine safety and efficiency. His work addresses critical challenges in the detection and mitigation of equipment failures and material impurities. Li’s most cited paper (35 citations) introduces a novel method for detecting belt conveyor deviation using an inspection robot and deep learning, enabling real-time identification of this common safety hazard at any point along the conveyor. This contribution significantly enhances operational reliability and reduces accident risk. In a subsequent influential study (15 citations), Li improved the YOLOv7 network model for gangue selection robots, achieving more accurate detection of gangue and foreign matter in coal. This work directly tackles the dual problems of compromised coal thermal properties and equipment damage, advancing the automation of coal quality control. Li’s research integrates computer vision, robotics, and deep learning to create practical, deployable solutions for the mining industry, demonstrating high impact through citations and laying the groundwork for safer, more efficient coal production processes.
Research Focus
Key Achievements
Top Papers
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- 2