Papers
2
Total Citations
14
H-Index
2
About
Longqi Cheng is a researcher at the forefront of applying deep learning to structural engineering, with a specialized focus on the automated detection and segmentation of curtain wall frames. His work addresses a critical bottleneck in building inspection and retrofitting: the labor-intensive process of identifying and delineating complex facade components. Cheng’s major contributions lie in developing advanced computer vision architectures that fuse cross-modal data and leverage context-aware pyramid networks to achieve high-precision, automated analysis. His 2024 paper on cross-modal feature fusion for curtain wall frame detection has already garnered 10 citations, while his subsequent work on a context collaboration pyramid network for segmentation has accumulated 4 citations, signaling a growing impact in the niche but vital field of automated structural assessment. By bridging deep learning with civil engineering, Cheng is enabling faster, safer, and more accurate building diagnostics. His research is particularly notable for its practical applicability, offering a direct pathway to modernizing construction inspection workflows. For students and researchers, Cheng’s work exemplifies how cutting-edge AI can solve real-world infrastructure challenges, making him a key figure to watch in the intersection of computer vision and structural health monitoring.
Research Focus
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Top Papers
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