Xiaogang Lv
Papers
1
Total Citations
6
H-Index
1
About
Xiaogang Lv is a computer vision researcher whose work centers on pedestrian detection, a critical technology for video surveillance, autonomous driving, and robotics. His most cited paper, "Pedestrian Detection Using Regional Proposal Network with Feature Fusion" (2018), addresses a key challenge in the field: improving detection accuracy by integrating multi-scale feature maps into the Region Proposal Network (RPN) framework. This contribution enhances the ability of deep learning models to identify pedestrians in complex, real-world scenes, directly supporting safer and more reliable autonomous systems. With 6 citations, this work has informed subsequent advances in object detection and feature fusion techniques. Lv’s research exemplifies the practical application of deep learning to solve pressing problems in intelligent transportation and public safety, making him a notable contributor to the ongoing evolution of computer vision.
Research Focus
Key Achievements
Top Papers
- 1Pedestrian Detection Using Regional Proposal Network with Feature Fusion6 citations · 2018