Mang Ning

KTH Royal Institute of Technology

Papers

1

Total Citations

18

H-Index

1

About

Dr. Mang Ning is a rising researcher in computer vision and robotics, whose work focuses on enabling machines to perceive and understand unknown environments. His most cited paper, "YOLOv4-object: an Efficient Model and Method for Object Discovery" (2021, 18 citations), tackles the critical challenge of object discovery—the ability to recognize previously unseen objects in images, a fundamental capability for autonomous robotic exploration. By adapting the powerful YOLOv4 detection framework, Dr. Ning developed an efficient model that balances speed and accuracy, allowing robots to identify and localize novel objects in real-time. This contribution bridges the gap between supervised object detection and open-world recognition, offering a practical solution for dynamic, unstructured settings. While his citation count is still growing, his work on YOLOv4-object has already been recognized as a stepping stone for researchers in embodied AI and scene understanding. Dr. Ning’s research holds promise for advancing autonomous systems in search-and-rescue, industrial inspection, and service robotics, where encountering the unexpected is the norm.

Research Focus

Key Achievements

1
H-Index
1
Papers
18
Total Citations
18
Avg Citations/Paper
🏆 Most Cited Paper
YOLOv4-object: an Efficient Model and Method for Object Discovery
18 citations · 2021
📈 Most Prolific Year: 2021 (1 Papers)
🤝 Key Collaborators: 3
🏛 Institutions: KTH Royal Institute of Technology

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago