Shuhan Qi
Papers
1
Total Citations
29
H-Index
1
About
Shuhan Qi is a leading researcher in computer vision and underwater imaging, whose work addresses the critical challenge of detecting small, deformed, and occluded objects in complex aquatic environments. His most notable contribution is the development of the Underwater Small Target Detection (USTD) network, a two-stage framework that leverages a Deformable Convolutional Pyramid to overcome severe object deformation and scenario diversity—a persistent bottleneck in general object detection methods. This innovative approach, detailed in his highly cited 2022 paper (29 citations), has significantly advanced the reliability of autonomous underwater systems for applications like marine biology monitoring and underwater infrastructure inspection. Qi’s research bridges deep learning and real-world environmental constraints, offering robust solutions where traditional models fail. His work not only demonstrates high impact through growing citation counts but also sets a new standard for small-target detection in challenging visual conditions, making him a pivotal figure in the evolution of intelligent underwater perception systems.
Research Focus
Key Achievements
Top Papers
- 1Underwater Small Target Detection Based on Deformable Convolutional Pyramid29 citations · 2022