Yuehua Li
Papers
3
Total Citations
12
H-Index
2
About
Yuehua Li’s research lies at the intersection of computer vision and robotics, with a focus on enhancing how machines perceive and interact with their environments. Her key contributions span salient object detection, video object detection, and robotic calibration. In her most-cited work, a 2022 paper with 6 citations, she introduced a transformer-based adaptive interactive promotion network for RGB-Thermal salient object detection, fusing visual and thermal infrared data to improve robot decision-making in complex visual tasks. She also developed a novel memory mechanism for video object detection from indoor mobile robots (2021, 4 citations), addressing the challenge of dynamic scene understanding. More recently, Li advanced robotic precision with an online hand-eye calibration method that uses 3D textureless object tracking (2023, 2 citations), enabling decoupled calibration for dynamic object grasping without reliance on 2D fiducial markers. Her work demonstrates a clear trajectory toward making robots more adaptive and accurate in real-world settings, with applications in autonomous navigation, manipulation, and surveillance. Li’s research is particularly notable for integrating thermal imaging and memory-based reasoning to overcome limitations in traditional vision systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3