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/bf11171a-1f57-4a8a-9768-757850d13665

Fernhout, Merit (2023): Annotated images of microscopy images from carbon fibre composites. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/bf11171a-1f57-4a8a-9768-757850d13665.v1

Dataset

• Artificial Intelligence and Image Processing
• Mechanical Engineering
• Aerospace Engineering
• Materials Engineering
• Engineering
• Information and Computing Sciences

annotation, carbon fibre, composites, fibres, images, machine learning, semantic segmentation, voids

2023

CC0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Merit Fernhout

Four data sets containing 500, 500, 1000 and 32 images of 256 x 256 pixels of parts of microscopy images of cross sections of carbon fibre composite laminates. The data sets contain corresponding annotated masks for every image. Each pixel is either annotated in the classes: matrix material, fibre or void and is respectively labelled with 0, 1 and 2. The masks are made by locating the centres of the fibres with a find local maxima method in ImageJ. These locations are then used to draw perfect circles with the average fibre radius around them to indicate the fibres. The voids are detected using a thresholding algorithm based on a minimum method. The data sets can be used to train, validate and test semantic segmentation algorithms.

• 2023-10-02 first online, published, posted

4TU.ResearchData

Image files (.tif .jpg and .png) in a ZIP file

University of Twente, Faculty of Engineering Technology;
Thermoplastic Composites Research Center (TPRC)