Dang Huynh
Papers
2
Total Citations
25
H-Index
2
About
Dang Huynh is a researcher at the forefront of human-robot interaction (HRI) and computer vision, with a particular focus on integrating deep learning with mechanical robotic systems. His most cited work, "Efficient Human-Robot Interaction using Deep Learning with Mask R-CNN: Detection, Recognition, Tracking and Segmentation" (2018), has accumulated 25 citations, demonstrating its influence in the field. Huynh’s major contribution lies in proposing a robust framework that combines Mask R-CNN—a state-of-the-art neural network for object detection and segmentation—with a parallel micro-manipulator mechanism. This integration enables robots to not only detect, recognize, and track human faces but also to translate visual data into precise, 3D spatial movements for natural social interaction. By addressing the challenging problem of target segmentation, his work bridges the gap between high-level visual perception and low-level robotic control. This research is particularly notable for its practical applications in social robotics, where safe and intuitive interaction is paramount. Huynh’s approach stands out for its ability to handle both detection and segmentation tasks simultaneously, making his contributions valuable for students and researchers interested in the intersection of AI, robotics, and human-centered design.
Research Focus
Key Achievements
Top Papers
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