Papers
1
Total Citations
2
H-Index
1
About
Van-Dung Do is a computer vision researcher whose work focuses on advancing intelligent transportation systems through robust traffic sign recognition. His key research areas include real-time object detection, autonomous vehicle perception, and driver assistance technologies. Do’s most notable contribution is his development of a lightweight model for real-time traffic sign recognition, which addresses the critical need for efficient, deployable solutions in autonomous navigation and advanced driver assistance systems (ADAS). This work, published in 2020, has garnered 2 citations and highlights his ability to balance computational efficiency with accuracy—a vital challenge in embedded vision applications. His research has practical implications for path planning, robot navigation, and enhancing driver safety. Do’s approach emphasizes real-world deployability, making his contributions particularly relevant for students and engineers working on edge-AI systems for autonomous vehicles. By tackling the complexities of traffic sign detection under varying environmental conditions, he continues to shape the future of intelligent transportation.
Research Focus
Key Achievements
Top Papers
- 1A Lightweight Model For Real-time Traffic Sign Recognition2 citations · 2020