Papers
4
Total Citations
43
H-Index
2
About
Elven Kee is a researcher at the forefront of intelligent robotics and sustainable automation, specializing in low-cost, resource-constrained computer vision systems. Their work focuses on making deep learning object detection practical for real-world applications, particularly in pick-and-place operations and industrial robotics. Kee’s most impactful contribution, “A Comparative Analysis of Cross-Validation Techniques for a Smart and Lean Pick-and-Place Solution with Deep Learning” (2023, 36 citations), demonstrates how to achieve high-accuracy object detection on embedded devices like the Raspberry Pi—a critical advance for cost-effective automation. This work is complemented by studies on hyperparameter and image enhancement optimization (2024) and machine vision assistance (2022), which together form a cohesive body of research on lean, sustainable robotic solutions. Most recently, Kee has pushed boundaries with a zero-shot detection framework for corner casting in shipping container operations (2025), enabling intelligent robot perception without prior training data. By consistently proving that powerful AI can run on minimal hardware, Kee is helping democratize smart manufacturing and autonomous systems for industries with limited resources.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4