Data underlying the student thesis: Automated monitoring of corrosion on piling sheets
doi:10.4121/21387930.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
For a link that will always point to the latest version, please use
doi: 10.4121/21387930
doi: 10.4121/21387930
Datacite citation style:
Richie Maskam; Amiri-Simkooei, A. (Alireza); Sander van Nederveen; Mohammad Fotouhi; Maarten Visser (2022): Data underlying the student thesis: Automated monitoring of corrosion on piling sheets. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21387930.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
usage stats
724
views
367
downloads
categories
geolocation
Lemmer-Delfzijl, The Netherlands
licence
CC BY 4.0
This is a piling sheet data used for classification and object detection. The classes are based on Digiggids. Object detection was used to detect dimensional features and estimate dimensions that could tell us about the type of piling sheet.
For the notebooks you can check my github page.
history
- 2022-10-25 first online, published, posted
publisher
4TU.ResearchData
format
*.jpg; *.txt
references
organizations
TU Delft, Faculty of Civil Engineering and GeosciencesWitteveen+Bos N.V.
DATA
files (4)
- 2,991 bytesMD5:
b928447f135e0474b0370232a376d110
README.md - 2,753,221,349 bytesMD5:
d87c8178ca1093732b1681062d1237b4
01-Classification_data.zip - 218,825,367 bytesMD5:
763d1d3557beb66fa4ca4c362f1463ac
02-Object_detection_data.zip - 64,885,163 bytesMD5:
1594b755f941e5c667d2dab070ec0909
03-Model_Test.zip -
download all files (zip)
3,036,934,870 bytes unzipped