Kaixuan Chen
Papers
1
Total Citations
7
H-Index
1
About
Kaixuan Chen is an emerging researcher specializing in underwater computer vision and deep learning-based object detection. Their most notable work, "U-ATSS: A lightweight and accurate one-stage underwater object detection network" (2024), addresses the unique and demanding challenges of detecting objects in underwater environments, where issues such as light scattering, color distortion, and low visibility complicate conventional detection approaches. By developing a one-stage detection architecture that balances computational efficiency with high accuracy, Chen's research makes meaningful strides toward practical, deployable solutions for marine exploration, underwater robotics, and aquatic monitoring systems. The paper has already garnered 7 citations shortly after publication, signaling early recognition from the research community and suggesting growing interest in the field of underwater perception. Chen's contributions reflect a broader trend in applied deep learning, where domain-specific adaptations of general frameworks yield significant performance improvements in challenging real-world conditions. For students and researchers working at the intersection of marine technology and artificial intelligence, Chen's work represents a promising foundation for advancing the state of the art in underwater visual intelligence.
Research Focus
Key Achievements
Top Papers
- 1