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Trained Model for the Semantic Segmentation of Concrete Cracks (Conglomerate)

software
posted on 30.09.2021, 18:02 authored by Eric BianchiEric Bianchi, Matthew Hebdon

This contains a trained DeeplabV3+ model for the semantic segmentation of concrete cracks. The models all were trained using image sizes of 512x512. The classes for this model are: [background, and crack]. This model was trained using the conglomerate concrete crack dataset (DOI: 10.7294/16625056). The model was able to correctly identify approximately 70% of the ground truth labeled cracks in the test set. More details of the training, the results, the dataset, and the code may be referenced in the journal article. The repository of the training and testing code may be accessed using the GitHub repository referenced in the journal article.


If you are using the model in your work, please include both the journal article and the model citation.

Funding

National Science Foundation Grant No. IIS-1840044

History

Publisher

University Libraries, Virginia Tech

Language

English (US)

Location

Virginia Tech