ZhengBai Yao
Papers
1
Total Citations
10
H-Index
1
About
ZhengBai Yao is a researcher at the forefront of efficient computer vision, specializing in lightweight deep learning models for real-time object detection on resource-constrained platforms. His most-cited work, "Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices" (2022), tackles the critical challenge of deploying high-speed, accurate detection on robots and edge devices with limited computational power. By optimizing the YOLO architecture, Yao’s contributions enable practical AI applications in autonomous systems, robotics, and IoT, bridging the gap between algorithmic performance and real-world hardware constraints. With over 10 citations for this seminal paper, his work is gaining traction among engineers and researchers seeking to balance speed and accuracy in edge computing. Yao’s research is particularly notable for its focus on deployability—making advanced vision models accessible for low-power devices without sacrificing robustness. His achievements reflect a growing demand for efficient AI, positioning him as a key contributor to the next generation of intelligent, autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices10 citations · 2022