Yinghong Fang
Papers
1
Total Citations
4
H-Index
1
About
Yinghong Fang is a researcher whose work bridges deep learning, computer vision, and mobile robotics, with a focus on enabling intelligent perception and autonomous navigation. In their most-cited study, "A Deep Learning-based Visual Perception Approach for Mobile Robots" (2018), Fang developed a novel framework that integrates convolutional neural networks with computer vision techniques to enhance a mobile robot's ability to perceive and follow visual paths in real time. This work, which has garnered 4 citations, was validated through an experimental platform using differential wheeled mobile robots and a LabVIEW-based upper computing system, demonstrating robust motion control and path perception. Fang’s contributions are particularly notable for advancing the practical deployment of deep learning in resource-constrained robotic systems, offering a scalable solution for visual navigation. While their citation count reflects an emerging impact, the research holds significance for students and engineers exploring the intersection of AI and robotics, providing a foundational approach for future work in autonomous vehicle guidance and industrial automation. Fang’s work continues to inspire developments in efficient, vision-driven robotic control.
Research Focus
Key Achievements
Top Papers
- 1A Deep Learning-based Visual Perception Approach for Mobile Robots4 citations · 2018