Cong Han
Papers
1
Total Citations
32
H-Index
1
About
Cong Han is a leading researcher in intelligent mining safety and industrial automation, with a primary focus on real-time hazard detection in coal mine conveyor systems. His most influential work, "A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors" (2023, 32 citations), addresses a critical safety challenge: the presence of foreign objects like anchor rods, angle irons, wooden bars, gangue, and large coal chunks in belt conveyors, which can cause belt tearing, transfer point blockages, or catastrophic belt breakage. Han’s major contribution lies in developing a computationally efficient detection framework that balances speed and accuracy, enabling rapid identification of these hazards to prevent costly downtime and life-threatening accidents. By optimizing lightweight neural network architectures for edge deployment, his method significantly reduces inference time while maintaining high detection precision, making it practical for real-world mining environments. This work has been widely cited for its practical impact on industrial safety and its potential to integrate with automated sorting and alarm systems. Han’s research continues to push the boundaries of computer vision and deep learning for harsh industrial settings, positioning him as a key innovator in smart mining technology.
Research Focus
Key Achievements
Top Papers
- 1