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dc.contributor.authorGeng, Alexander-
dc.contributor.authorMoghiseh, Ali-
dc.date.accessioned2025-01-03T10:26:51Z-
dc.date.available2025-01-03T10:26:51Z-
dc.date.issued2024-12-20-
dc.identifier.urihttps://fordatis.fraunhofer.de/handle/fordatis/429-
dc.identifier.urihttp://dx.doi.org/10.24406/fordatis/381-
dc.description.abstractThis repository contains a dataset of images featuring concrete panels, designed to support research and development in image-based analysis and defect detection. The dataset is divided into two main categories: crack images with corresponding masks, and crack images without masks, which include both crack and no-crack images.en
dc.language.isoenen
dc.relation.ispartofhttps://doi.org/10.26204/KLUEDO/8207-
dc.relation.ispartofhttps://doi.org/10.48550/arXiv.2307.16723-
dc.relation.ispartofhttp://dx.doi.org/10.24406/fordatis/313.2-
dc.relation.ispartofhttps://doi.org/10.58895/ksp/1000174496-10-
dc.relation.isbasedonhttps://www.itwm.fraunhofer.de/toolip-
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/en
dc.subjectCrack Image Dataseten
dc.subjectConcrete Panelsen
dc.subjectThin Cracksen
dc.subjectCrack in Concreteen
dc.subjectNarrow Width Cracksen
dc.subjectHaarrissde
dc.subject.ddcDDC::000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::005 Computerprogrammierung, Programme, Datenen
dc.subject.ddcDDC::600 Technik, Medizin, angewandte Wissenschaften::670 Industrielle Fertigung::679 Andere Produkte aus einzelnen Werkstoffenen
dc.subject.ddcDDC::600 Technik, Medizin, angewandte Wissenschaften::680 Industrielle Fertigung für einzelne Verwendungszwecke::680 Industrielle Fertigung für einzelne Verwendungszweckeen
dc.titleHairWidthCracks dataseten
dc.typeImageen
dc.contributor.funderFraunhofer-Gesellschaft FhGen
dc.description.technicalinformationThe dataset consists of concrete images originally captured at approximately 16,000 × 32,000 pixels. These images depict large concrete panels, each with dimensions of 1200 × 2000 millimeters. To facilitate the identification and extraction of specific regions containing cracks, the software ToolIP [1], especially ToolImA was utilized. The tool enabled the scanning of these large images and the selection of 224 × 224 pixel regions where cracks are present. In total, a curated subset of 1,500 images was extracted, 1,000 of them with cracks and 500 without. Each extracted image measures 224 × 224 pixels and is presented in an 8-bit grayscale format, with the .pgm extension used for storage. As part of the second step in the data preparation process, ToolImA was again used to perform binary pixel-wise annotations (crack pixel or background) on the selected regions. The annotations include detailed crack information, with an average crack thickness of 1–2 pixels, providing a high level of granularity for analysis and training of machine learning models. The final dataset, therefore, consists of accurately annotated 224 × 224 pixel images, formatted in 8-bit grayscale with the corresponding masks, ready for use in deep learning and other computational applications. [1] “ToolIP – Tool for Image Processing.” Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM, https://www.itwm.fraunhofer.de/toolip. Accessed 20 Dec. 2024.en
fordatis.instituteITWM Fraunhofer-Institut für Techno- und Wirtschaftsmathematiken
fordatis.rawdatafalseen
Appears in Collections:Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM

Files in This Item:
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HairWidthCracks_dataset_teaser(samples).pdfTeaser images and masks of the first part of the dataset.155,31 kBAdobe PDFDownload/Open
HairWidthCracks_dataset_teaser(samples)_without_masks.pdfTeaser images of the second part of the dataset (images with and without cracks).183,52 kBAdobe PDFDownload/Open
HairWidthCracks.zipDataset.42,86 MBZIPDownload/Open
README.mdReadme file on the usage of the dataset.1,72 kBUnknownDownload/Open
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