Yuhui Zeng
Papers
1
Total Citations
3
H-Index
1
About
Yuhui Zeng is a researcher specializing in computer vision and deep learning, with a particular focus on lightweight object detection models for industrial applications. Their most notable contribution is the development of GSC-YOLO, a novel lightweight network designed for the precise detection of cup and piston heads in manufacturing environments. This work, published in 2023, addresses the critical need for efficient, real-time visual inspection systems in industrial automation. By optimizing the YOLO architecture for resource-constrained settings, Zeng’s research balances accuracy with computational efficiency, making it highly relevant for deployment on edge devices. While still early in its impact, the paper has already garnered 3 citations, signaling growing interest from peers in applied AI and quality control. Zeng’s work exemplifies how tailored deep learning models can solve domain-specific challenges, bridging the gap between cutting-edge algorithms and practical engineering needs. Their research holds promise for advancing smart manufacturing and automated defect detection.
Research Focus
Key Achievements
Top Papers
- 1GSC-YOLO: a lightweight network for cup and piston head detection3 citations · 2023