Shidan Sun
Papers
1
Total Citations
51
H-Index
1
About
Shidan Sun is a leading researcher in computer vision and autonomous robotics, with a primary focus on underwater object detection and real-time visual perception. Sun’s most influential work, “An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection” (2022), has garnered 51 citations for addressing a critical challenge: enabling underwater robots to perform rapid, precise object detection in visually degraded environments. By enhancing the YOLO algorithm, Sun’s research directly supports autonomous exploration of underwater ecosystems, improving the efficiency and reliability of robotic systems used in marine resource management and environmental monitoring. This contribution is vital for advancing real-time detection capabilities in low-visibility, high-pressure aquatic settings. Sun’s work stands out for its practical impact on field robotics, bridging the gap between algorithmic innovation and real-world deployment. With a growing citation record, Sun is recognized for pushing the boundaries of deep learning in challenging domains, making autonomous underwater exploration more feasible and accurate.
Research Focus
Key Achievements
Top Papers
- 1An Improved YOLO Algorithm for Fast and Accurate Underwater Object Detection51 citations · 2022