Yaohan Zeng
Papers
1
Total Citations
2
H-Index
1
About
Yaohan Zeng is a researcher focused on advancing computer vision and autonomous robotics, with a particular emphasis on real-time object detection and tracking for mobile platforms. Their most cited work, "Target Detection and Tracking of Ground Mobile Robot Based on Improved Single Shot Multibox Detector Network" (2021), tackles critical bottlenecks in deploying deep learning for robotics—specifically, the slow pace of dataset annotation and the computational lag of SSD-based detection algorithms. Zeng proposed a fast data set labeling algorithm and a refined SSD network architecture, significantly accelerating both the training pipeline and inference speed for ground robots. While their citation count is currently modest at 2, this foundational contribution addresses a practical, industry-relevant challenge: enabling mobile robots to detect and track targets with greater efficiency. This work signals Zeng’s commitment to bridging the gap between theoretical deep learning advances and real-world robotic applications, making their research particularly valuable for students and engineers working on autonomous navigation, surveillance, or field robotics where speed and accuracy are paramount.
Research Focus
Key Achievements
Top Papers
- 1