Xiaojun Yan
Papers
1
Total Citations
9
H-Index
1
About
Xiaojun Yan is a researcher specializing in deep learning and computer vision, with a particular focus on object detection in challenging underwater environments. Their most notable contribution is the development of an attention mechanism improved Ghost-YOLOv5 model for underwater fish detection, published in 2022. This work addresses the critical challenge of deploying accurate yet computationally efficient deep convolutional neural networks on resource-constrained robotic platforms. By integrating attention mechanisms into the lightweight Ghost-YOLOv5 architecture, Yan achieved a practical balance between detection accuracy and computational efficiency, enabling real-time fish detection in underwater robotics applications. The paper has garnered 9 citations, reflecting its relevance to researchers working on marine biology monitoring, aquaculture automation, and underwater robotics. Yan's research is particularly significant for its focus on bridging the gap between state-of-the-art object detection algorithms and the real-world constraints of underwater deployment, where limited computational resources and challenging visual conditions demand innovative solutions.
Research Focus
Key Achievements
Top Papers
- 1