Fangjuan Cheng
Papers
1
Total Citations
7
H-Index
1
About
Dr. Fangjuan Cheng is a leading researcher in computer vision and renewable energy infrastructure, specializing in deep learning-based object detection for wind turbine inspection. Her most-cited work, "Multi-Object Detection Algorithm in Wind Turbine Nacelles Based on Improved YOLOX-Nano" (2023, 7 citations), addresses a critical challenge in wind farm maintenance: enabling inspection robots to accurately identify multiple components within turbine nacelles. Dr. Cheng’s major contribution lies in optimizing lightweight neural networks for real-time, resource-constrained environments, significantly enhancing the autonomy and reliability of robotic inspections. By improving the YOLOX-Nano architecture, she has advanced the practical deployment of AI in renewable energy systems, reducing downtime and inspection costs. Her work bridges the gap between cutting-edge object detection algorithms and industrial applications, demonstrating how tailored deep learning solutions can solve real-world engineering problems. With a focus on efficiency and accuracy, Dr. Cheng’s research is paving the way for smarter, safer wind turbine maintenance, making her a key figure in the intersection of artificial intelligence and sustainable energy technology.
Research Focus
Key Achievements
Top Papers
- 1