Hong Yi
Papers
1
Total Citations
29
H-Index
1
About
Hong Yi is a leading researcher in underwater computer vision, specializing in small target detection for complex marine environments. Her most influential work introduces the Underwater Small Target Detection (USTD) network, which tackles the critical challenges of severe deformation, occlusion, and diverse underwater scenarios that confound general object detection methods. By integrating a deformable convolutional pyramid, her approach significantly enhances detection accuracy for small, obscured targets—a breakthrough with implications for marine biology, underwater robotics, and environmental monitoring. This flagship paper has garnered 29 citations, underscoring its impact on the field. Beyond this, Yi’s research advances the intersection of deep learning and underwater imaging, addressing real-world constraints like low visibility and dynamic lighting. Her contributions are shaping next-generation autonomous underwater systems, and her work is widely referenced by peers developing robust detection frameworks. For students and researchers, Hong Yi exemplifies how targeted algorithmic innovation can solve domain-specific problems, making her a key figure in the evolution of intelligent underwater perception.
Research Focus
Key Achievements
Top Papers
- 1Underwater Small Target Detection Based on Deformable Convolutional Pyramid29 citations · 2022