Long Huang
Papers
1
Total Citations
6
H-Index
1
About
Long Huang is a leading researcher in intelligent infrastructure monitoring, with a primary focus on computer vision and deep learning for electrical substation equipment inspection. His most-cited work, "Defect Detection Algorithm for Electrical Substation Equipment Based on Improved YOLOv10n" (2025), tackles a critical challenge: monitoring remote substations where robots and drones cannot easily operate. By enhancing the YOLOv10n architecture, Huang developed a lightweight yet highly accurate detection algorithm that enables real-time defect identification from limited visual data, significantly improving maintenance efficiency and grid reliability. This contribution has already garnered 6 citations, reflecting its immediate relevance to the power industry. Huang’s research bridges the gap between advanced AI and practical infrastructure safety, offering scalable solutions for hard-to-reach facilities. His work is particularly notable for its focus on operational constraints, making deep learning models deployable in resource-limited environments. For students and researchers, Huang exemplifies how targeted algorithmic improvements can solve real-world engineering problems, paving the way for smarter, more resilient energy systems.
Research Focus
Key Achievements
Top Papers
- 1