Yipeng Zhou
Papers
1
Total Citations
19
H-Index
1
About
Yipeng Zhou is a researcher specializing in computer vision and deep learning, with a particular focus on efficient object detection for security and surveillance applications. His most-cited work, "Object detection method based on lightweight YOLOv4 and attention mechanism in security scenes" (2023, 19 citations), introduces a novel approach that balances detection accuracy with computational efficiency—a critical challenge for real-time security systems. By integrating attention mechanisms into a lightweight YOLOv4 architecture, Zhou demonstrates how to enhance feature extraction while reducing model complexity, making advanced detection feasible for resource-constrained environments. This contribution addresses the growing demand for practical, deployable AI in public safety and smart surveillance. While his citation count reflects the early stage of his career, the work’s focus on optimization and real-world applicability signals its potential for broader impact. Zhou’s research bridges the gap between state-of-the-art deep learning and operational constraints, offering a template for future lightweight vision models. His efforts underscore a commitment to making AI more accessible and effective in critical security contexts.
Research Focus
Key Achievements
Top Papers
- 1