Xiaoyan Meng
Papers
1
Total Citations
5
H-Index
1
About
Xiaoyan Meng is a leading researcher in underwater robotics and computer vision, whose work addresses the critical challenge of distinguishing bionic underwater robots from real marine life. Her most-cited paper, "Adversarial learning-based method for recognition of bionic and highly contextual underwater targets" (2023), introduces a novel adversarial learning framework that enables autonomous underwater vehicles (AUVs) to overcome the deceptive visual similarity between biomimetic robots and actual creatures. This work not only enhances detection accuracy in highly contextual underwater environments but also tackles the practical issue of large model sizes in existing detection methods. With growing recognition in the field, Meng’s contributions are pivotal for advancing AUV autonomy, security, and ecological monitoring. Her research has already garnered attention, with her top-cited paper accumulating 5 citations, signaling its emerging impact. By bridging the gap between artificial and natural underwater perception, Xiaoyan Meng is shaping the future of intelligent underwater systems, making her a key figure for students and researchers interested in adversarial learning, marine robotics, and real-world computer vision applications.
Research Focus
Key Achievements
Top Papers
- 1