Xiyu Chen
Papers
1
Total Citations
2
H-Index
1
About
Xiyu Chen is a rising researcher in the field of computer vision and deep learning, with a focused expertise in lightweight neural network architectures for specialized environmental perception. Chen’s primary research area is underwater target detection, a critical technology for autonomous underwater robotics, marine resource surveys, and environmental monitoring. Their most notable contribution is the development of a novel detection framework based on YOLOv8, enhanced with a multi-scale cross-channel attention mechanism. This work addresses the fundamental challenge of balancing high detection accuracy with computational efficiency in resource-constrained underwater platforms. By integrating this attention module, Chen’s model significantly improves the network’s ability to focus on salient features in complex, low-visibility underwater scenes while maintaining a lightweight footprint suitable for real-time deployment. Although a recent publication from 2024, this work has already garnered early citations, signaling its immediate relevance and potential for high impact in the field. Chen’s research is paving the way for more practical and robust autonomous systems in challenging aquatic environments, making them a promising voice in the intersection of efficient deep learning and marine robotics.
Research Focus
Key Achievements
Top Papers
- 1