Eun Su Oh
Papers
1
Total Citations
3
H-Index
1
About
Eun Su Oh is a researcher at the forefront of integrating computer vision and autonomous systems, with a primary focus on 3D object detection and mobile robotics. Their most notable contribution is the development of a novel framework that synergizes DBSCAN clustering with the YOLOv5 deep learning architecture, enabling robust 3D object detection from point cloud data. This work, published in 2024, demonstrates a significant advancement in adapting state-of-the-art detection algorithms for real-world mobile platforms, addressing critical challenges in dynamic environments such as occlusion and computational efficiency. Although early in its lifecycle, the paper has already garnered 3 citations, signaling growing interest from the autonomous vehicle and robotics communities. Oh’s research bridges the gap between theoretical detection models and practical deployment, offering a scalable solution for applications ranging from autonomous navigation to warehouse automation. By focusing on the adaptation of lightweight yet accurate detection systems, Oh is contributing to the next generation of intelligent, perception-driven mobile systems, making their work a valuable resource for students and researchers exploring efficient real-time 3D perception.
Research Focus
Key Achievements
Top Papers
- 1