Papers
2
Total Citations
8
H-Index
2
About
Xiaoyu Xu is a researcher specializing in computer vision and robotic perception, with a focused interest in structural segmentation for construction automation. His work centers on developing deep learning architectures that enable precise, real-time analysis of building facades—particularly curtain wall frames—to support autonomous robot pose estimation and navigation. Xu’s major contributions include the design of an edge information fusion perception network and a dual-flow aggregation network, both tailored to segment complex, repetitive structural elements with high accuracy. His 2024 paper on edge information fusion has already garnered 6 citations, reflecting its immediate relevance to the field. By bridging visual segmentation and robotics, Xu’s research addresses critical challenges in automated construction and inspection, where reliable frame detection is essential for safe and efficient robot operation. His dual-flow aggregation approach, which integrates multi-scale features, has been validated for pose estimation tasks, marking a notable step toward fully autonomous building maintenance systems. Xu’s work is particularly valuable for students and researchers interested in the intersection of computer vision, deep learning, and field robotics.
Research Focus
Key Achievements
Top Papers
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