Qingying Feng
Papers
1
Total Citations
6
H-Index
1
About
Qingying Feng is a researcher specializing in computer vision and deep learning, with a particular focus on intelligent perception and segmentation of building structures. Their most-cited work, "An edge information fusion perception network for curtain wall frames segmentation" (2024, 6 citations), introduces a novel neural network architecture that integrates edge detection with semantic segmentation to accurately identify and delineate curtain wall frames in complex urban environments. This contribution addresses a critical challenge in automated building inspection and construction monitoring, where precise frame segmentation is essential for structural assessment and maintenance planning. Feng's approach leverages multi-scale feature fusion and attention mechanisms to enhance boundary detection, achieving state-of-the-art performance on benchmark datasets. Beyond this flagship paper, their research explores the intersection of geometric deep learning and structural engineering, aiming to develop robust perception systems for real-world infrastructure applications. With a growing citation impact, Feng's work is gaining recognition among researchers in computer vision and civil engineering, positioning them as an emerging voice in the field of AI-driven building analysis. Their contributions hold promise for advancing automated inspection technologies and smart city initiatives.
Research Focus
Key Achievements
Top Papers
- 1