Changgen Deng
Papers
1
Total Citations
6
H-Index
1
About
Changgen Deng is a researcher whose work lies at the intersection of computer vision, robotics, and intelligent inspection systems. His primary research focuses on developing robust detection and control strategies for automated visual inspection in complex environments, with particular emphasis on pointer meter reading—a critical task for industrial monitoring. Deng's most notable contribution is his adaptive reflection detection and control strategy for pointer meters, published in 2023, which addresses a persistent challenge in the field: how to maintain accurate readings when reflective surfaces interfere with robot-mounted cameras. By integrating an improved k-means clustering method with YOLOv5s object detection, his approach enables robots to autonomously identify and mitigate reflective interference, significantly enhancing the reliability of automated inspection systems. This work has already garnered 6 citations, demonstrating its relevance to ongoing research in industrial automation. Deng's contributions are particularly valuable for advancing the practical deployment of inspection robots in real-world settings, where environmental variability often undermines system performance. His research bridges the gap between theoretical computer vision and applied robotics, offering tangible solutions for industries reliant on precise instrument monitoring.
Research Focus
Key Achievements
Top Papers
- 1