Jinwei Huang
Papers
1
Total Citations
2
H-Index
1
About
Jinwei Huang is a researcher specializing in the intersection of mobile robotics and deep learning, with a primary focus on dynamic target detection and tracking in aquatic environments. His most notable work, "Dynamic Target Detection and Tracking in Water for Mobile Robot Based on Deep Learning" (2020), introduces innovative approaches for enabling mobile robots to perceive and follow moving objects in challenging underwater conditions—a critical capability for applications in marine exploration, environmental monitoring, and autonomous navigation. By leveraging deep learning architectures, Huang addresses key challenges such as water turbidity, variable lighting, and target occlusion, advancing the robustness of real-time robotic vision systems. While his citation count (2) reflects the emerging nature of his contributions, his research lays foundational groundwork for integrating AI-driven perception with autonomous underwater vehicles. Huang’s work is particularly relevant for students and engineers interested in field robotics, computer vision, and the practical deployment of neural networks in unstructured environments. His focus on water-based dynamic tracking highlights a niche yet growing area of robotics, where precision and adaptability are paramount.
Research Focus
Key Achievements
Top Papers
- 1