Code to paper: Review of image segmentation techniques for the layup defect detection in the Automated Fiber Placement process
doi: 10.4121/14412923.v1
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doi: 10.4121/14412923
doi: 10.4121/14412923
Datacite citation style:
Sebastian Meister; Mahdieu Wermes (2021): Code to paper: Review of image segmentation techniques for the layup defect detection in the Automated Fiber Placement process. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/14412923.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
categories
licence
BSD-3-Clause
This code was used to generate the results for the paper
"Review of image segmentation techniques for the layup defect
detection in the Automated Fiber Placement process"
"Review of image segmentation techniques for the layup defect
detection in the Automated Fiber Placement process"
history
- 2021-05-11 first online, published, posted
publisher
4TU.ResearchData
associated peer-reviewed publication
Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process
funding
- German Aerospace Center core funding
organizations
TU Delft, Faculty of Aerospace Engineering, Department of Aerospace Structures & Materials;German Aerospace Center (DLR), Center for Lightweight Production Technology (ZLP)
DATA
files
- 7,020 bytes md5 cfg_gen.py
- 2,150 bytes md5 config.ini
- 3,773 bytes md5 DefectDetectionAnalysis.py
- 1,169 bytes md5 instructions.ini
- 10,782 bytes md5 newstyle_DefectDetectionAnalysis.py
- 2,204 bytes md5 README.txt
- download all files (zip)