Xiaoke Liu
Papers
1
Total Citations
10
H-Index
1
About
Xiaoke Liu is a leading researcher in intelligent manufacturing and robotic vision, with a focus on automated part recognition and precision assembly systems. Their most-cited work, "Research on Non-Pooling YOLOv5 Based Algorithm for the Recognition of Randomly Distributed Multiple Types of Parts" (2022, 10 citations), addresses a critical bottleneck in industrial automation: enabling robots to identify and collect randomly scattered precision components after cleaning. By modifying the YOLOv5 architecture to eliminate pooling layers, Liu’s algorithm significantly improves detection accuracy for small, irregularly arranged parts—a challenge that has long limited flexible automation in precision machinery. This contribution directly supports the development of adaptive robotic systems capable of handling unstructured environments, reducing the need for fixed-position part feeders. Liu’s research bridges computer vision and mechanical engineering, offering practical solutions for smart factories. Their work has been recognized for its potential to enhance production efficiency in high-precision industries, and they continue to advance algorithms that empower robots to perceive and manipulate complex, real-world part distributions.
Research Focus
Key Achievements
Top Papers
- 1