Xinliang Yao
Papers
1
Total Citations
17
H-Index
1
About
Xinliang Yao is a researcher at the forefront of intelligent robotics and computer vision, with a specialized focus on object detection and visual servoing for automated manufacturing. His work centers on developing efficient, real-time perception systems that enable robots to interact with complex industrial components. Yao’s major contribution is the creation of MGBM-YOLO, a faster, light-weight object detection model specifically designed for the challenging task of robotic grasping of bolster springs. This model, detailed in his most-cited 2022 paper (17 citations), integrates image-based visual servoing to achieve high-speed, accurate detection on resource-constrained hardware, directly addressing the industry’s need for cost-effective automation. By optimizing the YOLO architecture for industrial parts, Yao has demonstrated how to balance speed and precision in cluttered environments, advancing the practical deployment of deep learning in manufacturing. His work is notable for bridging the gap between cutting-edge computer vision algorithms and real-world robotic applications, offering a scalable solution that reduces computational overhead without sacrificing performance. Yao’s research continues to influence the design of agile, vision-guided robotic systems.
Research Focus
Key Achievements
Top Papers
- 1