Papers
1
Total Citations
2
H-Index
1
About
Yonggen Cao is a researcher focused on intelligent robotics and autonomous driving systems, with a particular emphasis on efficient computer vision solutions for resource-constrained environments. His most notable contribution is the development of an end-to-end light license plate detection and recognition (LPDR) method based on deep learning, designed specifically for mobile edge computing (MEC) chips. This work addresses the critical challenge of deploying complex neural networks on small computing capacity devices, such as those used in autonomous vehicles, where large GPU servers are impractical. By creating a lightweight network architecture, Cao enables real-time LPDR tasks to be executed directly on MEC chips, significantly reducing latency and power consumption while maintaining accuracy. Although his most-cited paper currently has 2 citations, it represents an important step toward practical, edge-based AI systems for intelligent transportation. His research bridges the gap between deep learning performance and hardware limitations, making autonomous driving technologies more accessible and efficient.
Research Focus
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Top Papers
- 1