Yaqun Fang
Papers
1
Total Citations
5
H-Index
1
About
Yaqun Fang is a researcher specializing in deep learning, computer vision, and intelligent inspection systems for critical infrastructure. Their most notable contribution is the development of a lightweight deep learning model for substation meter detection and recognition, addressing the challenge of deploying complex AI models on resource-constrained embedded devices used in robotic inspections. This work, published in 2022 and garnering 5 citations, demonstrates a practical approach to balancing model accuracy with computational efficiency—a key concern for real-world automation. Fang’s research directly supports the advancement of intelligent robotics in power grids, enabling safer and more reliable substation monitoring. By focusing on model compression and real-time performance, their contributions help bridge the gap between cutting-edge deep learning and industrial application. Their work is particularly relevant for students and researchers interested in edge AI, smart grid automation, and the deployment of neural networks in low-power environments.
Research Focus
Key Achievements
Top Papers
- 1