SD-GCN: Saliency-based dilated graph convolution network for pavement crack extraction from 3D point clouds
• A developed SD-GCN model for accuracy pavement crack extraction from point clouds. • Feature maps and saliency matrices construction for high-level feature encodings. • Multi-scale graph-based efficient network for inherent point feature and edge feature representations. • Cylinder-based dilated c...
Ausführliche Beschreibung
Autor*in: |
Ma, Lingfei [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Network-wide assessment of ATM mechanisms using an agent-based model - Delgado, Luis ELSEVIER, 2021transfer abstract, s.l. |
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Übergeordnetes Werk: |
volume:111 ; year:2022 ; pages:0 |
Links: |
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DOI / URN: |
10.1016/j.jag.2022.102836 |
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ELV05818127X |
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• A developed SD-GCN model for accuracy pavement crack extraction from point clouds. • Feature maps and saliency matrices construction for high-level feature encodings. • Multi-scale graph-based efficient network for inherent point feature and edge feature representations. • Cylinder-based dilated convolution strategy for computational efficiency improvement. |
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• A developed SD-GCN model for accuracy pavement crack extraction from point clouds. • Feature maps and saliency matrices construction for high-level feature encodings. • Multi-scale graph-based efficient network for inherent point feature and edge feature representations. • Cylinder-based dilated convolution strategy for computational efficiency improvement. |
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• A developed SD-GCN model for accuracy pavement crack extraction from point clouds. • Feature maps and saliency matrices construction for high-level feature encodings. • Multi-scale graph-based efficient network for inherent point feature and edge feature representations. • Cylinder-based dilated convolution strategy for computational efficiency improvement. |
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SD-GCN: Saliency-based dilated graph convolution network for pavement crack extraction from 3D point clouds |
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