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

posted on 2021-09-30, 18:02 authored by Eric 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.


National Science Foundation Grant No. IIS-1840044



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