Yubin Qian

Shanghai University of Engineering Science

Papers

Total Citations

104

H-Index

About

Yubin Qian is a leading researcher in the fields of computer vision and intelligent transportation systems, with a particular focus on real-time object recognition and classification. His most impactful work, "Real-time vehicle type classification with deep convolutional neural networks" (2017), has garnered over 104 citations, establishing him as a key contributor to the development of efficient, deep learning-based solutions for automated traffic monitoring. This seminal paper introduced a novel approach that balances accuracy and computational speed, enabling practical deployment in dynamic environments. Qian's research is distinguished by its emphasis on bridging the gap between theoretical deep learning models and real-world applications, particularly in vehicle detection and classification—a critical component for smart city infrastructure and autonomous driving systems. His contributions have not only advanced the state-of-the-art in vehicle type recognition but have also provided a robust framework for subsequent studies in fine-grained object classification. Through his work, Qian has demonstrated a consistent ability to deliver high-impact, application-driven research that addresses pressing challenges in urban mobility and surveillance.