In Data in brief
We record Optical coherence tomography (OCT) images of various textile fabrics. Each textile fabric consisted of one material only: wool, cotton or polyester. We took OCT images from three different fabrics for each material type giving a total of 9 different fabrics. We scan each material at least a hundred times at different places on each surface. In order to have approximately consistent data between samples, the scans for each image were fixed to 2 mm scan length and saved in a portable network format. We divide the material data into three categories. Groups 1, 2, and 3 consisted only of cotton, wool, and polyester fabrics, respectively. These were placed in folders, becoming the labelled dataset for deep learning training classes. We publish this OCT fabric image dataset publicly. Researchers can utilize the data to train deep learning networks, test existing machine learning algorithms, or develop new systems for automated material classification and recycling.
Sabuncu Metin, Ozdemir Hakan
2022-Dec
Cotton, Deep learning, Material classification, Optical coherence imaging, Polyester, Recycling, Wool
