Shanmin Li
Papers
1
Total Citations
9
H-Index
1
About
Shanmin Li is a researcher whose work sits at the intersection of deep learning and marine robotics, with a primary focus on efficient object detection in challenging underwater environments. Li’s most notable contribution is the development of an attention mechanism-enhanced Ghost-YOLOv5 model for underwater fish detection, a task complicated by low visibility, variable lighting, and limited computational resources on robotic platforms. This work, published in 2022 and garnering 9 citations, addresses a critical bottleneck: the trade-off between detection accuracy and model efficiency. By integrating attention mechanisms into the lightweight Ghost-YOLOv5 architecture, Li demonstrated that it is possible to achieve high-precision fish detection without the heavy computational overhead typical of large-scale deep convolutional neural networks. This innovation is particularly valuable for real-time applications on autonomous underwater vehicles, where processing power is constrained. Li’s research is a key step toward making underwater robotic monitoring more practical and scalable, directly supporting marine biology, aquaculture, and environmental conservation efforts.
Research Focus
Key Achievements
Top Papers
- 1