Yutan Wang
Papers
2
Total Citations
20
H-Index
2
About
Yutan Wang is a researcher advancing the frontiers of computer vision and autonomous systems, with a focus on real-time semantic segmentation and image restoration. His work centers on developing efficient, multi-scale feature extraction networks that enable machines to perceive and interpret complex visual environments—from agricultural settings to autonomous navigation. Wang’s most cited paper, “MFENet: Multi-scale feature extraction network for images deblurring and segmentation of swinging wolfberry branch” (2023, 11 citations), tackles the challenging problem of motion blur in dynamic agricultural scenes, improving both deblurring and segmentation accuracy for robotic harvesting. In a complementary study (9 citations), he extends this architecture to real-time semantic segmentation of road scenes for autonomous robots, demonstrating the versatility of his approach across domains. By designing lightweight yet powerful networks, Wang addresses the critical trade-off between computational efficiency and high-quality perception, making his contributions valuable for embedded and mobile robotics. His work not only advances practical applications in precision agriculture and autonomous driving but also provides a foundation for future research in multi-scale feature learning.
Research Focus
Key Achievements
Top Papers
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