Thanh-Thanh Ngo-Quang
Papers
1
Total Citations
2
H-Index
1
About
Thanh-Thanh Ngo-Quang is a researcher focused on advancing computer vision for intelligent transportation systems, with a particular emphasis on traffic sign recognition (TSR) for autonomous vehicles and driver assistance technologies. Their most cited work, "A Lightweight Model For Real-time Traffic Sign Recognition" (2020), addresses the critical need for efficient, deployable models in resource-constrained environments like embedded systems in cars. By designing a compact yet accurate architecture, Ngo-Quang contributes to making real-time TSR feasible for practical applications such as advanced driver assistance systems (ADAS), path planning, and robot navigation. This work has garnered 2 citations, reflecting its niche but growing relevance in the field. Ngo-Quang’s research bridges the gap between theoretical computer vision and real-world deployment, tackling challenges of speed and computational efficiency without sacrificing recognition performance. Their contributions are particularly valuable for students and researchers exploring lightweight neural networks for edge computing in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1A Lightweight Model For Real-time Traffic Sign Recognition2 citations · 2020