Papers
1
Total Citations
35
H-Index
1
About
Yan Sun is a leading researcher in underwater computer vision and deep learning-based object detection, with a focus on overcoming the unique challenges of aquatic environments. Their most-cited work, "Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention" (2023, 35 citations), introduces a novel hybrid architecture that integrates MobileViT’s lightweight transformer capabilities with double coordinate attention mechanisms. This innovation significantly enhances detection accuracy for small, low-contrast targets in turbid or poorly lit underwater settings—a persistent bottleneck in marine robotics and environmental monitoring. By addressing the limitations of conventional detectors in complex underwater imaging, Sun’s contributions have direct implications for autonomous underwater vehicles, search-and-rescue operations, and ecological surveillance. Their research demonstrates a rare ability to bridge theoretical advances in attention-based neural networks with practical, real-world deployment constraints. With growing citation impact, Yan Sun is establishing themselves as a key figure in the intersection of deep learning and marine technology, offering solutions that are both computationally efficient and robust to environmental degradation.
Research Focus
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Top Papers
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