Shuxin Huang
Papers
1
Total Citations
3
H-Index
1
About
Shuxin Huang has made impactful contributions at the intersection of computer vision and intelligent power systems, with a focus on enhancing the reliability of robotic inspections through deep learning. His most cited work, "Research on Image Recognition of Power Inspection Robot Based on Improved YOLOv3 Model" (2020, 3 citations), addresses a critical challenge in the field: the frequent miss-detection of small and diverse targets during automated power line inspections. Huang proposed two innovative network architectures—YOLOv3-g, optimized for GPU cores, and YOLOv3 mini, tailored for CPU cores—significantly improving detection accuracy and computational efficiency. This dual-model approach demonstrates his ability to balance performance with hardware constraints, a key consideration for real-world deployment. While his citation count is modest, the work’s practical relevance to energy infrastructure automation underscores its value. Huang’s research exemplifies how targeted algorithmic refinements can solve domain-specific problems, making him a notable figure in applied AI for industrial robotics. His contributions offer a valuable case study for students and researchers exploring efficient object detection in resource-constrained environments.
Research Focus
Key Achievements
Top Papers
- 1