Dinh-Manh-Cuong Tran
Papers
2
Total Citations
27
H-Index
2
About
Dinh-Manh-Cuong Tran is a rising researcher in computer vision and mobile robotics, specializing in lightweight deep learning architectures for autonomous navigation. His work focuses on enabling efficient, real-time perception for resource-constrained mobile robots, particularly through monocular camera systems. Tran’s most-cited paper, “IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation” (2023), has garnered 23 citations and introduces a compact neural network for semantic segmentation, directly addressing the challenge of obstacle avoidance in complex environments. Building on this, his 2025 paper “ELDE-Net: Efficient Light-Weight Depth Estimation Network for Deep Reinforcement Learning-Based Mobile Robot Path Planning” (4 citations) advances monocular depth estimation, integrating it with deep reinforcement learning for robust path planning. Tran’s contributions are notable for their practical emphasis on balancing accuracy with computational efficiency, making them suitable for real-world deployment on low-power hardware. His work is increasingly cited in the fields of autonomous systems and embedded AI, reflecting its relevance to both academic research and industrial applications. As a young investigator, Tran is establishing himself as a key voice in the development of vision-driven, intelligent mobile robots.
Research Focus
Key Achievements
Top Papers
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- 2