Data · dataset · 2023
Cracks In Concrete Structures (CICS) Dataset
Listed in Teesside University Research Data Repository
A drone (phantom4 pro) with a 20 M Pixel and full HD camera was used to prepare images of concrete structures in campus of Imam Khomeini International University and Qazvin city in Iran.
Description
Various of concrete structures, using concrete cement type 2 and grade 250 to 300 were considered to collect images. The information provided by phantom4 pro was saved as color images and videos which resulted in the storage of 900 images with 4k resolution.
The stored color images were first converted to grayscale images, and then, each of the 900 grayscale images was divided into small images with 330×330 pixels resolution. Then, 12000 grayscale images with the size of 330×330 pixels were created. These images were classified visually into three classes (without crack, simple crack, and multibranched crack).
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Finally, these images named as Cracks In Concrete Structures (CICS) dataset consists of three folders. The folder names reflect the shape of the contained images. There are 4000 images without crack, 4000 simple crack images, and 4000 images with multibranched crack.
Links
Where it is published
- DOI doi.org/10.17632/9brnm3c39k.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Deep learning · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Machine learning · Social Science
- Inferred from text
- Image 65%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/9brnm3c39k.1 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |