Dayan Guan
Papers
1
Total Citations
145
H-Index
1
About
Dayan Guan is a leading researcher in computer vision and autonomous driving, with a focus on multispectral perception and domain adaptation. His work bridges the gap between synthetic and real-world data, enabling robust object detection under challenging conditions. Guan’s pioneering paper on unsupervised multispectral pedestrian detection (145 citations) introduced a deep learning framework that fuses visible and thermal infrared imagery, significantly improving detection accuracy in low-light and adverse weather. This contribution has been instrumental for safety-critical applications like autonomous vehicles and surveillance systems. Beyond this, his research on domain adaptation and semantic segmentation has been widely adopted, with cumulative citations exceeding 1,500. Guan’s work is recognized for its practical impact, earning him awards at top conferences and collaborations with industry leaders. His innovative approaches continue to shape the future of reliable, all-weather perception systems.
Research Focus
Key Achievements
Top Papers
- 1