Kimin Yun

Electronics and Telecommunications Research Institute

Papers

Total Citations

H-Index

About

Kimin Yun is a researcher whose work lies at the intersection of computer vision and safety-critical artificial intelligence, with a primary focus on object detection and uncertainty estimation. His most impactful contribution is the development of methods to quantify localization uncertainty in anchor-free object detection—a crucial advancement for systems like autonomous vehicles and surgical robots that must operate reliably despite sensor noise and incomplete data. His 2023 paper on this topic has garnered 24 citations, while an earlier 2020 version has accumulated 7 citations, demonstrating growing recognition of his work. By addressing the limitations of existing detectors that fail to account for spatial ambiguity, Yun has helped bridge the gap between theoretical computer vision models and real-world deployment in unstable environments. His research is particularly valuable for practitioners building robust perception systems where knowing not just what an object is, but how confident the model is about its location, can mean the difference between safe and catastrophic operation.