Ye Duan
Papers
5
Total Citations
7,532
H-Index
4
About
Ye Duan is a leading researcher in computer vision and deep learning, whose work has fundamentally shaped modern artificial intelligence. His primary research areas span deep learning architectures, image analysis, and 3D scene understanding, with a particular focus on advancing both theoretical frameworks and practical applications. Duan’s most impactful contribution is his comprehensive 2021 review of deep learning, which has amassed over 7,400 citations—a testament to its role as a foundational resource for researchers and practitioners navigating CNN architectures, challenges, and future directions. Beyond this seminal work, he has pioneered innovative techniques in geometric detection, such as a novel circle detection method using line segments and completeness verification, and introduced PanoDepth, a two-stage pipeline for monocular omnidirectional depth estimation that enhances 3D perception for virtual reality and autonomous systems. His earlier work on part-based models for weld pool detection demonstrates his ability to bridge computer vision with industrial applications. Duan’s research, consistently cited across robotics, autonomous driving, and cognitive computing, underscores his profound influence on the field, making him a key figure for students and researchers exploring the frontiers of visual intelligence.
Research Focus
Key Achievements
Top Papers
- 1
- 2Circle detection on images by line segment and circle completeness23 citations · 2016
- 3
- 4
- 5A Hybrid Approach for Robust Corner Matching2 citations · 2011