Yalei Fan
Papers
1
Total Citations
16
H-Index
1
About
Yalei Fan is a leading researcher in intelligent power infrastructure and unmanned aerial vehicle (UAV)-based inspection systems, with a focus on overhead transmission line maintenance. Their most cited work, “OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance” (2019, 16 citations), introduces a novel imaging classification framework that enables autonomous drones to detect and diagnose faults in high-voltage power lines. This contribution directly addresses the growing global demand for reliable electricity by reducing the need for dangerous manual inspections and improving maintenance efficiency. Fan’s research integrates computer vision, machine learning, and robotics to solve real-world challenges in energy infrastructure. Beyond this flagship paper, their work has advanced the practical deployment of UAVs in industrial settings, earning recognition for its potential to transform utility operations. With a citation record that continues to grow, Yalei Fan is establishing themselves as a key voice in the intersection of smart grid technology and autonomous systems—an area critical for modernizing aging power networks and ensuring energy security worldwide.
Research Focus
Key Achievements
Top Papers
- 1