Rudi Villing
Papers
10
Total Citations
76
H-Index
6
About
Rudi Villing's research sits at the intersection of computer vision, robotics, and assistive technology, with a focus on making intelligent systems practical for real-world deployment. His most significant contributions center on efficient deep learning for resource-constrained platforms, particularly through pioneering work in network pruning for object detection. His benchmark dataset for ball detection in the RoboCup SPL (16 citations) and Faster YOLO-LITE architecture (10 citations) have become foundational for real-time robotic vision, demonstrating how to maintain accuracy while dramatically reducing computational demands. Villing has also advanced keypoint detection for fisheye cameras (10 citations), addressing a critical gap in autonomous driving and robotics applications. Beyond technical optimization, his work extends to socially impactful domains: his research on assistive robotics for older care (6 citations) and the CASIE project on ethical social intelligence for healthcare (6 citations) show a commitment to human-centered AI. His recent work integrating collaborative robots with computer vision for infant formula quality testing (5 citations) exemplifies his ability to bridge cutting-edge computer vision with practical industrial applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Evaluating pruned object detection networks for real-time robot vision13 citations · 2018
- 3Faster YOLO-LITE: Faster Object Detection on Robot and Edge Devices10 citations · 2022
- 4
- 5
- 6
- 7
- 8
- 9
- 10Evaluating Extended Pruning on Object Detection Neural Networks3 citations · 2018