Papers
1
Total Citations
3
H-Index
1
About
Jung-Ick Moon is a researcher focused on advancing computer vision and autonomous systems, with a particular emphasis on efficient object detection for robotics. His most notable contribution is the development of ODAR (Object Detection for Autonomous Driving Robots), a lightweight framework introduced in 2021 that addresses the critical challenge of enabling real-time object detection on resource-constrained platforms like self-driving robots. This work bridges the gap between deep learning's computational demands and practical deployment, targeting applications in video surveillance, autonomous navigation, and automated payment systems. While his citation count is modest—with ODAR garnering 3 citations to date—the framework represents a meaningful step toward making object detection more accessible for embedded systems. Moon’s research underscores the importance of balancing accuracy with efficiency, a key concern for robotics and edge computing. His work is particularly relevant for students and engineers seeking to implement deep learning models in real-world autonomous systems, where speed and low power consumption are paramount. Though early in his career, Moon’s focus on practical, deployable AI solutions positions him as a contributor to the growing field of lightweight computer vision.
Research Focus
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Top Papers
- 1