Dinh-Tan Nguyen
Papers
1
Total Citations
3
H-Index
1
About
Dinh-Tan Nguyen is a researcher focused on advancing safety and autonomy in human-robot interaction, with a particular emphasis on computer vision and deep learning. His work centers on developing robust detection systems that enable robots to perceive and respond to human presence in shared workspaces. His most-cited paper, "Person Detection for Monitoring Individuals Accessing the Robot Working Zones Using YOLOv8" (2024), demonstrates a practical application of state-of-the-art object detection to enhance workplace safety. By leveraging the YOLOv8 architecture, Nguyen’s research addresses a critical challenge in collaborative robotics: ensuring that machines can reliably identify humans entering their operational zones, thereby preventing accidents and enabling more fluid human-robot collaboration. Though early in his career, with this work already garnering citations, Nguyen is establishing a reputation for translating cutting-edge AI models into real-world safety solutions. His contributions are particularly relevant for industries adopting collaborative robots, where reliable person detection is foundational to both operational efficiency and worker protection.
Research Focus
Key Achievements
Top Papers
- 1