Papers
1
Total Citations
3
H-Index
1
About
Mingyong Xin is a researcher specializing in intelligent inspection systems and computer vision applications for power infrastructure. His work focuses on developing automated recognition technologies for industrial environments, particularly in power substations. His most cited paper, "Automatic Recognition of Indoor Digital Instrument Reading for Inspection Robot of Power Substation" (2017, 3 citations), introduces a hybrid algorithm that combines template matching with deep learning to accurately read digital meters in substation environments. This contribution addresses a critical need for reliable, autonomous monitoring in hazardous or hard-to-reach areas, enhancing operational safety and efficiency. Xin's research bridges pattern recognition and deep learning, offering practical solutions for industrial automation. While his citation count is modest, his work represents foundational steps in applying AI to real-world inspection tasks, with potential for broader impact as robotics and smart grid technologies advance. His achievements highlight the importance of domain-specific algorithm design in specialized industrial settings.
Research Focus
Key Achievements
Top Papers
- 1