YOLOv11n-CDL: accurate and lightweight pavement defect detection via enhanced multi-scale attention and feature fusion
Pavement defect detection requires both high accuracy and real-time performance in complex road environments, yet existing lightweight models often struggle with blurred textures, background interference, and small cracks. To address these limitations, this study proposes YOLOv11n-CDL, an enhanced...
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| Główni autorzy: | , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
Vilnius Gediminas Technical University
2026-02-01
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| Seria: | Journal of Civil Engineering and Management |
| Hasła przedmiotowe: | |
| Dostęp online: | https://journals.vilniustech.lt/index.php/JCEM/article/view/26166 |
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