Papers
1
Total Citations
149
H-Index
1
About
Peng Ni is a leading researcher in computer vision and robotics, with a primary focus on efficient deep learning for real-time object detection. His most influential work, "Lightweight object detection algorithm for robots with improved YOLOv5" (2023), has garnered 149 citations, reflecting its significant impact on the field. This paper addresses the critical challenge of deploying high-performance detection models on resource-constrained robotic platforms, proposing a novel lightweight architecture that maintains accuracy while drastically reducing computational overhead. Ni's contributions are particularly notable for bridging the gap between state-of-the-art neural networks and practical robotic applications, enabling faster, more reliable perception in autonomous systems. His work is widely cited by researchers developing edge-AI solutions for drones, mobile robots, and industrial automation. By optimizing the YOLOv5 framework for embedded devices, Ni has advanced the feasibility of real-time visual intelligence in real-world robotics, making his research essential reading for those working at the intersection of deep learning and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Lightweight object detection algorithm for robots with improved YOLOv5149 citations · 2023