Self‐supervised domain adaptive approach for extrapolated crack segmentation with fine‐tuned inpainting generative model DOI Creative Commons
Seungbo Shim

Computer-Aided Civil and Infrastructure Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 25, 2025

Abstract The number and proportion of aging infrastructures are increasing, thereby necessitating accurate inspection to ensure safety structural stability. While computer vision deep learning have been widely applied concrete cracks, domain shift issues often result in the poor performance pretrained models at new sites. To address this, a self‐supervised adaptation method using generative artificial intelligence based on inpainting is proposed. This approach generates site‐specific crack images labels by fine‐tuning Stable Diffusion model with DreamBooth. resulting data set then used train detection neural network learning. Evaluations across two target sets eight show average F1‐score improvements 25.82% 17.83%. A comprehensive tunnel ceiling field test further demonstrates effectiveness method. By enhancing real‐world capabilities, this supports better management.

Язык: Английский

Self‐supervised domain adaptive approach for extrapolated crack segmentation with fine‐tuned inpainting generative model DOI Creative Commons
Seungbo Shim

Computer-Aided Civil and Infrastructure Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 25, 2025

Abstract The number and proportion of aging infrastructures are increasing, thereby necessitating accurate inspection to ensure safety structural stability. While computer vision deep learning have been widely applied concrete cracks, domain shift issues often result in the poor performance pretrained models at new sites. To address this, a self‐supervised adaptation method using generative artificial intelligence based on inpainting is proposed. This approach generates site‐specific crack images labels by fine‐tuning Stable Diffusion model with DreamBooth. resulting data set then used train detection neural network learning. Evaluations across two target sets eight show average F1‐score improvements 25.82% 17.83%. A comprehensive tunnel ceiling field test further demonstrates effectiveness method. By enhancing real‐world capabilities, this supports better management.

Язык: Английский

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