Meijun Guo
Papers
2
Total Citations
2
H-Index
1
About
Meijun Guo is a researcher at the forefront of computer vision and robotics, with a focus on integrating perception and automation for real-world applications. Their work spans object detection, semantic segmentation, and automated construction, where they develop innovative frameworks that bridge the gap between visual understanding and robotic action. Guo’s most notable contribution is the YOLO-FS framework, a unified model that combines object detection and semantic segmentation to enhance environment awareness for robot navigation and autonomous driving. This work, published in 2025, demonstrates a novel approach to leveraging both localization and pixel-level understanding. Additionally, Guo has made significant strides in construction automation through a vision-based methodology for detecting and localizing rebar intersections, enabling precise robotic rebar tying. This research integrates RGB-D sensing, camera calibration, and coordinate transformation to achieve accurate spatial localization. While early in their career, Guo’s work is already garnering attention, with each of these papers receiving citations that underscore their potential impact. Their contributions are paving the way for more intelligent and autonomous systems in both mobile robotics and industrial construction.
Research Focus
Key Achievements
Top Papers
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- 2