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2D Segmentation of Concrete Samples for Training AI Models
This web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes. The motivation behind this web validation system is to prepare a large number of pixel-level multi-class annotated microscopy images for training artificial intelligence (AI) based segmentation models (U-Net and SegNet models). While the purpose of the AI models is to predict accurately four damage labels, such as, paste damage, aggregate damage, air voids, and no-damage, our goal is to assert trust in such predictions (a) by using contextual labels and (b) by enabling visual validations of predicted damage labels.
Resources
4 resources available
-
DOI Access for 2D Segmentation of Concrete Samples for Training AI Models
FILE -
UNet CNN Semantic-Segmentation Inference plugin
FILE -
UNet CNN Semantic-Segmentation Training plugin
FILE -
2D Segmentation of Concrete Samples for Training AI Models
TIFF FILE FORMAT
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "006:55" ] |
| contactPoint |
{ "fn": "Peter Bajcsy", "hasEmail": "mailto:peter.bajcsy@nist.gov" } |
| description | This web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes. The motivation behind this web validation system is to prepare a large number of pixel-level multi-class annotated microscopy images for training artificial intelligence (AI) based segmentation models (U-Net and SegNet models). While the purpose of the AI models is to predict accurately four damage labels, such as, paste damage, aggregate damage, air voids, and no-damage, our goal is to assert trust in such predictions (a) by using contextual labels and (b) by enabling visual validations of predicted damage labels. |
| distribution |
[ { "title": "DOI Access for 2D Segmentation of Concrete Samples for Training AI Models", "accessURL": "https://doi.org/10.18434/M32155" }, { "title": "UNet CNN Semantic-Segmentation Inference plugin", "accessURL": "https://github.com/usnistgov/WIPP-unet-inference-plugin" }, { "title": "UNet CNN Semantic-Segmentation Training plugin", "accessURL": "https://github.com/usnistgov/WIPP-unet-train-plugin" }, { "title": "2D Segmentation of Concrete Samples for Training AI Models", "format": "TIFF file format", "accessURL": "https://isg.nist.gov/deepzoomweb/data/concreteScoring", "description": "his web-based validation system has been designed to perform visual validation of automated multi-class segmentation of concrete samples from scanning electron microscopy (SEM) images. The goal is to segment automatically SEM images into no-damage and damage sub-classes, where the damage sub-classes consist of paste damage, aggregate damage, and air voids. While the no-damage sub-classes are not included in the goal, they provide context for assigning damage sub-classes." } ] |
| identifier | ark:/88434/mds2-2155 |
| issued | 2019-12-31 |
| keyword |
[ "CS-MET computational metrology" ] |
| landingPage | https://data.nist.gov/od/id/mds2-2155 |
| language |
[ "en" ] |
| license | https://www.nist.gov/open/license |
| modified | 2019-11-18 00:00:00 |
| programCode |
[ "006:045" ] |
| publisher |
{ "name": "National Institute of Standards and Technology", "@type": "org:Organization" } |
| theme |
[ "Information Technology:Computational science" ] |
| title | 2D Segmentation of Concrete Samples for Training AI Models |
