| Autor: |
Saúl Cano-Ortiz, Eugenio Sainz-Ortiz, Lara Lloret Iglesias, Pablo Martínez Ruiz del Árbol, Daniel Castro-Fresno |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Results in Engineering, Vol 23, Iss , Pp 102745- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1230 |
| DOI: |
10.1016/j.rineng.2024.102745 |
| Popis: |
Computer-aided deep learning has significantly advanced road crack segmentation. However, supervised models face challenges due to limited annotated images. There is also a lack of emphasis on deriving pavement condition indices from predicted masks. This article introduces a novel semantic diffusion synthesis model that creates synthetic crack images from segmentation masks. The model is optimized in terms of architectural complexity, noise schedules, and condition scaling. The optimal architecture outperforms state-of-the-art semantic synthesis models across multiple benchmark datasets, demonstrating superior image quality assessment metrics. The synthetic frames augment these datasets, resulting in segmentation models with significantly improved efficiency. This approach enhances results without extensive data collection or annotation, addressing a key challenge in engineering. Finally, a refined pavement condition index has been developed for automated end-to-end defect detection systems, promoting more effective maintenance planning. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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