Duc Dung Tran
Papers
1
Total Citations
3
H-Index
1
About
Duc Dung Tran is a robotics researcher whose work focuses on advancing object recognition in cluttered, real-world environments. His primary research areas include computer vision, deep learning, and robotic perception, with a particular emphasis on handling partially occluded objects—a common challenge in both household and industrial settings. Tran’s most cited paper, "A Faster R-CNN Approach for Partially Occluded Robot Object Recognition" (2019), introduces a region proposal network (RPN) that enables more robust feature extraction and classification, significantly improving a robot’s ability to identify objects even when they are partially hidden. This work has garnered 3 citations, reflecting its relevance to the growing field of robotic manipulation and autonomous systems. By tackling the critical problem of occlusion, Tran’s research contributes to making robots more reliable and effective in dynamic, unstructured environments. His approach demonstrates a practical application of deep learning to enhance robotic perception, offering a foundation for future innovations in assistive robotics, manufacturing, and service automation.
Research Focus
Key Achievements
Top Papers
- 1A Faster R-CNN Approach for Partially Occluded Robot Object Recognition3 citations · 2019