Raphael Wagner
Papers
2
Total Citations
17
H-Index
2
About
Raphael Wagner is a leading researcher at the intersection of computer vision, edge computing, and intelligent manufacturing, with a primary focus on enabling robust, real-time AI for Industry 5.0. His work tackles a critical bottleneck in industrial automation: the reliance on cloud-based systems for object detection and model training. Wagner’s major contributions center on developing end-to-end, edge-based vision systems that bring state-of-the-art deep learning directly to the factory floor. His most cited paper, "IndustrialEdgeML" (2023, 9 citations), presents a complete system for bin-picking applications, proving that high-performance AI can run locally without cloud dependency. Complementing this, his work on "Automatic Bounding Box Annotation with Small Training Datasets" (2023, 8 citations) addresses another key challenge—the high cost of data labeling—by enabling object detection models to rapidly adapt to changing environments with minimal human input. Together, these contributions are paving the way for more flexible, efficient, and autonomous human-robot collaboration in manufacturing, making Wagner a notable voice in the push toward truly intelligent, decentralized industrial systems.
Research Focus
Key Achievements
Top Papers
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