Hang Zhu
Papers
1
Total Citations
9
H-Index
1
About
Hang Zhu’s research lies at the intersection of computer vision and unmanned aerial vehicle (UAV) autonomy, with a particular focus on robust tracking and landing control in GPS-denied environments. His most cited work, “A Method for Designated Target Anti-Interference Tracking Combining YOLOv5 and SiamRPN for UAV Tracking and Landing Control” (2022), addresses a critical challenge: enabling UAVs to reliably track and land on designated targets despite visual interference. By fusing YOLOv5’s real-time detection with SiamRPN’s robust tracking, Zhu’s approach significantly improves anti-interference performance, offering a practical solution for both military and civilian missions where GPS signals are weak or unavailable. This work, with 9 citations, underscores his contribution to advancing vision-based UAV autonomy. Zhu’s research is particularly notable for bridging deep learning and real-time control, making autonomous landing more reliable in complex environments. His achievements highlight a growing demand for resilient, vision-driven navigation systems, positioning him as a promising voice in the field of intelligent aerial robotics.
Research Focus
Key Achievements
Top Papers
- 1