Yawen Zhao

Papers

Total Citations

H-Index

About

Yawen Zhao is a researcher advancing intelligent systems at the intersection of computer vision, robotics, and smart retail automation. Her work focuses on developing efficient, real-time solutions for complex indoor environments, with key contributions in lightweight deep learning models and multi-sensor fusion for autonomous navigation. Zhao’s most cited paper, “LSR-YOLO: A lightweight and fast model for retail products detection” (2025, 6 citations), introduces an optimized object detection framework that balances accuracy and computational efficiency, enabling practical deployment in smart city retail settings. Her second notable work, “Improved Localization Algorithm Based on Multi-Sensor Fusion for Shopping Robots” (2024, 2 citations), tackles critical challenges in indoor robotics—specifically, initial position estimation and map consistency—by integrating LiDAR and RGB-D data within an enhanced ORB-SLAM3 system. This approach significantly improves navigation reliability for shopping robots starting from arbitrary locations. Zhao’s research directly addresses the computational and operational demands of real-world automation, bridging the gap between theoretical advances and deployable systems. Her work holds strong potential for transforming retail logistics and autonomous service robotics, making her a promising voice in the field of intelligent urban technologies.