Xuncheng Wu

Shanghai University of Engineering Science

Papers

Total Citations

114

H-Index

About

Xuncheng Wu is a leading researcher in intelligent robotics and computer vision, with a focus on deep learning applications for real-world automation. His most influential work, "Real-time vehicle type classification with deep convolutional neural networks" (2017, 104 citations), established a foundational method for efficient, high-accuracy vehicle recognition, significantly advancing intelligent transportation systems. Wu has also made notable contributions to human-robot interaction, particularly through his research on robotic control via dynamic and static gesture recognition (2019, 10 citations). This work addresses a critical challenge in industrial automation: enabling flexible, intuitive control of robots through gesture-based interfaces, moving beyond rigid programming methods. By integrating deep learning with robotic systems, Wu’s research bridges the gap between computer vision and practical robotics, offering scalable solutions for smart manufacturing and autonomous systems. His work is widely cited for its technical rigor and real-world applicability, making him a key figure in the development of intelligent, human-centric robotic technologies.