Linfei Wang
Papers
2
Total Citations
3
H-Index
1
About
Linfei Wang is a researcher at the forefront of machine intelligence and robotic vision, whose work bridges the gap between deep learning and real-time autonomous systems. His primary research areas include visual servo control, video object detection, and embedded AI systems. Wang’s major contribution lies in developing integrated frameworks that balance computational efficiency with high accuracy, addressing critical challenges in dynamic environments. His 2021 paper on the "Robot Visual Servo Control System Based on Deep Detection Network and Spatial Pose Estimation" (2 citations) introduces a novel approach that combines deep detection networks with spatial pose estimation, enabling robots to perceive and interact with their surroundings more precisely. This work tackles essential challenges in visual detection, offering a pathway toward more intelligent and responsive robotic systems. Additionally, Wang’s "DTB-Net: A Detection and Tracking Balanced Network for Fast Video Object Detection in Embedded Mobile Devices" (1 citation) presents an innovative solution for real-time video analysis on resource-constrained platforms, overcoming limitations in processing speed and accuracy. Though early in his career, Wang’s research demonstrates significant potential for advancing autonomous robotics and mobile AI applications, making his work particularly relevant for students and researchers exploring efficient, deployable vision systems.
Research Focus
Key Achievements
Top Papers
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- 2