Ying Zang
Papers
1
Total Citations
4
H-Index
1
About
Ying Zang is a robotics researcher whose work focuses on the intersection of machine vision and robotic control, particularly for industrial automation. Her most notable contribution, detailed in her 2024 paper "Enhanced Hand–Eye Coordination Control for Six-Axis Robots Using YOLOv5 with Attention Module," addresses a critical challenge in precision manufacturing: enabling robots to accurately identify and grasp small workpieces. While standard YOLOv5 models suffer from low precision and missed detections in such tasks, Zang proposes an enhanced approach that integrates attention mechanisms to significantly improve target recognition accuracy. This work, which has already garnered 4 citations, demonstrates her ability to refine deep learning architectures for real-world robotic applications. Her research is particularly relevant to the growing field of intelligent manufacturing, where reliable hand-eye coordination is essential for tasks like assembly and quality inspection. By bridging the gap between computer vision algorithms and physical robotic systems, Zang is contributing to the development of more adaptive and precise automation solutions that can handle the complexities of small-scale object manipulation.
Research Focus
Key Achievements
Top Papers
- 1