Yuehan Zeng
Papers
1
Total Citations
4
H-Index
1
About
Yuehan Zeng is a researcher in robotics and computer vision, with a focus on intelligent automation and real-time object detection. Their major contribution lies in addressing critical challenges in industrial robotic arm sorting—specifically, high error rates and poor real-time performance—by integrating improved deep learning models with vision systems. In their most-cited work, "The Workpiece Sorting Method Based on Improved YOLOv5 For Vision Robotic Arm" (2022, 4 citations), Zeng designed a vision robotic arm testing platform and proposed a novel sorting method that enhances both accuracy and processing speed. This work demonstrates a practical application of state-of-the-art object detection algorithms to real-world manufacturing tasks, bridging the gap between theoretical AI advances and industrial deployment. Zeng’s research is particularly valuable for students and engineers interested in the intersection of robotics, embedded vision, and deep learning, offering a clear example of how to optimize YOLOv5 for domain-specific challenges. With a growing citation record, Yuehan Zeng is establishing a reputation for developing efficient, deployable solutions that improve the reliability and responsiveness of automated systems in production environments.
Research Focus
Key Achievements
Top Papers
- 1