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- W2737067081 abstract "Automatically detecting cracks in images of the concrete surfaces of bridge posts is an important safety task. Previous methods have only focused on very obvious crack images that were taken from very close to the surface. When photographing facilities with automated equipment such as drones, the camera is often more than one meter away from the surface in question. Thus, a precise method is required to detect cracks that are photographed at large distances from the surface. We propose a novel approach to detect 0.3 mm cracks in photographs taken at a distance of 1 m from the surface. The proposed method uses segmentation.. Filtering and morphological operations are applied to the image to make the cracks more distinguishable from the background. We separate crack candidates from the rest of the image using segmentation. Finally, we filter out noise and clutter using three masks. Experimental results demonstrate the effectiveness of the proposed method." @default.
- W2737067081 created "2017-07-31" @default.
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- W2737067081 date "2017-05-01" @default.
- W2737067081 modified "2023-10-01" @default.
- W2737067081 title "Automatic crack detection on concrete images using segmentation via fuzzy C-means clustering" @default.
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- W2737067081 doi "https://doi.org/10.1109/icasi.2017.7988574" @default.
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