Papers
1
Total Citations
6
H-Index
1
About
Yutong Guo is a researcher at the forefront of computer vision and autonomous systems, with a focus on real-time scene understanding for robotics. Their most-cited work, "A scene classification algorithm of visual robot based on Tiny Yolo v2" (2019, 6 citations), introduces an end-to-end multi-object framework that significantly improves scene classification accuracy for robots and unmanned vehicles. By adapting the lightweight Tiny YOLOv2 architecture, Guo addresses the limitations of traditional convolutional neural networks, enabling faster and more precise environmental perception during autonomous navigation. This contribution is critical for applications in robotics, where real-time decision-making depends on accurate scene interpretation. Guo’s work bridges the gap between deep learning efficiency and practical deployment in resource-constrained systems, demonstrating a commitment to advancing intelligent automation. While early in their career, this foundational paper has already influenced subsequent research in embedded vision and edge AI, marking Guo as an emerging voice in the field of visual robotics and autonomous vehicle technology.
Research Focus
Key Achievements
Top Papers
- 1A scene classification algorithm of visual robot based on Tiny Yolo v26 citations · 2019