Deyuan Liu
Papers
1
Total Citations
6
H-Index
1
About
Deyuan Liu is a researcher specializing in computer vision, intelligent robotics, and industrial automation, with a particular focus on enhancing the reliability of automated inspection systems. His most notable contribution is the development of an adaptive reflection detection and control strategy for pointer meters, published in 2023. This work addresses a critical challenge in complex environments where reflective phenomena can cause reading failures in inspection robots. By introducing an improved k-means clustering method for adaptive detection of reflective areas and a robot posture control strategy, Liu’s approach significantly improves the accuracy and robustness of pointer meter readings. His paper has already garnered 6 citations, reflecting its relevance to researchers and engineers working on autonomous inspection and industrial metrology. Liu’s work bridges the gap between theoretical computer vision and practical robotic applications, offering a scalable solution for real-world industrial settings. His research is particularly valuable for advancing the capabilities of inspection robots in challenging environments, making him a promising contributor to the fields of intelligent sensing and automation.
Research Focus
Key Achievements
Top Papers
- 1