Youngwan Lee

Electronics and Telecommunications Research Institute

Papers

Total Citations

H-Index

About

Youngwan Lee is a researcher advancing the field of computer vision, with a primary focus on object detection and uncertainty estimation in safety-critical applications. His work addresses a fundamental challenge: enabling object detectors to reliably quantify their own localization uncertainty, particularly in unstable environments plagued by sensor noise and incomplete data—conditions common in autonomous driving and surgical robotics. Lee’s most cited paper, "Localization Uncertainty Estimation for Anchor-Free Object Detection" (2023), has garnered 24 citations, demonstrating its growing influence. In this work, he identifies and overcomes key limitations in existing anchor-free detectors, proposing novel methods to estimate spatial confidence, thereby improving robustness and reliability. His earlier 2020 version of the same study, with 7 citations, laid the groundwork for this trajectory. By tackling the critical intersection of detection accuracy and uncertainty awareness, Lee’s contributions are directly relevant to deploying AI in high-stakes, real-world systems where failure is not an option. His research continues to shape how machines perceive and act under uncertainty, marking him as a notable voice in reliable computer vision.