Kangning Li
Papers
1
Total Citations
6
H-Index
1
About
Kangning Li is a leading researcher in computer vision and intelligent defect detection, with a primary focus on advancing automated inspection systems for critical infrastructure. Li’s most-cited work, "Defect Detection Algorithm for Electrical Substation Equipment Based on Improved YOLOv10n" (2025, 6 citations), tackles a pressing real-world challenge: monitoring equipment defects in remote electrical substations where robots or drones cannot easily operate. By enhancing the YOLOv10n architecture, Li developed a more accurate and efficient algorithm capable of identifying subtle equipment anomalies, significantly improving the reliability of automated inspections. This contribution directly supports the safety and operational continuity of urban power grids. Li’s research bridges deep learning and practical engineering, offering scalable solutions for infrastructure monitoring in hard-to-reach environments. With a growing citation record and a focus on deployable AI, Li is establishing a reputation for translating cutting-edge computer vision techniques into tangible, high-impact applications for the energy sector.
Research Focus
Key Achievements
Top Papers
- 1