Papers
1
Total Citations
2
H-Index
1
About
Shu Gui is a rising researcher in computer vision, with a focus on multimodal perception and pedestrian detection for intelligent systems. Their most cited work, "GLNet-YOLO: Multimodal Feature Fusion for Pedestrian Detection" (2025), addresses a critical challenge in autonomous driving and surveillance: the failure of single-modal sensors in complex environments like low light or occlusion. Gui’s key contribution is a novel fusion framework that integrates complementary visual modalities—such as RGB and thermal or depth data—to dramatically improve detection robustness. This work has already garnered early citations, signaling its relevance to the field’s push toward safer, more reliable AI. By tackling the limitations of traditional detectors, Gui is helping pave the way for next-generation perception systems in robotics and smart cities. Their research sits at the intersection of deep learning, sensor fusion, and real-world deployment, offering practical solutions for high-stakes applications. As a young scholar, Gui’s work demonstrates a clear trajectory toward impactful, applied computer vision that bridges the gap between laboratory benchmarks and real-world reliability.
Research Focus
Key Achievements
Top Papers
- 1GLNet-YOLO: Multimodal Feature Fusion for Pedestrian Detection2 citations · 2025