S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning
Report summarizing automated concrete crack mapping using deep learning. Crack mapping concrete structures is a way to document and monitor cracks. In the past, crack mapping has been very labor
intensive from data collection to documentation. The use of UAS and photogrammetry has allowed for faster and more comprehensive data collection and products including high-resolution orthoimages used to identify and document cracks. In
addition, deep learning models can be used to automatically identify cracks from the orthoimages. This paper presents the process used to develop a deep learning model for automatic crack detection from data collected by UAS.
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2 resources available
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RISE Item Details Page URL for "S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning "
TEXT/HTML -
PDF File for "S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning "
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| description | Report summarizing automated concrete crack mapping using deep learning. Crack mapping concrete structures is a way to document and monitor cracks. In the past, crack mapping has been very labor intensive from data collection to documentation. The use of UAS and photogrammetry has allowed for faster and more comprehensive data collection and products including high-resolution orthoimages used to identify and document cracks. In addition, deep learning models can be used to automatically identify cracks from the orthoimages. This paper presents the process used to develop a deep learning model for automatic crack detection from data collected by UAS. |
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| landingPage | https://data.usbr.gov/catalog/4420/item/6272 |
| modified | 2020-10-01T22:05:07Z |
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| title | S&T Project Number 20105 Final Report: Identifying Cracks in Concrete from Previously Collected UAS Data Using Deep Learning |
