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Instance segmentation of 3D woven fabric from tomography images by Mathematical Morphology and Deep Learning methods

Abstract : In the field of composite materials, mesoscale modeling based on X-ray computed tomography are very widespread nowadays. This descriptive method requires image processing to identify the different objects within the material. In the present study, two different instance segmentation approaches are proposed: 1) a method based on Mathematical Morphology and 2) a Deep Learning one. Both methods are applied to determine the yarns paths and their envelopes. We succeed in both tasks on a dry 3D ply-to-ply angle-interlock fabric at low compaction level. In absence of manual labelling of the yarns envelopes, we manage to train a Deep Convolutional Neural Network (DCNN) on the pseudo-labeling provided by the morphological method, improving the latter and showing the potential of deep learning for image segmentation in this context, when yarns cross-sections are labelled by distance functions. At a higher level of compaction, we also manage to recover the yarn paths thanks to deep learning.
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https://hal.archives-ouvertes.fr/hal-03345132
Contributor : Samy Blusseau Connect in order to contact the contributor
Submitted on : Thursday, September 16, 2021 - 11:44:40 AM
Last modification on : Wednesday, November 17, 2021 - 12:33:45 PM

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  • HAL Id : hal-03345132, version 1

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Samy Blusseau, Yanneck Wielhorski, Zyad Haddad, Santiago Velasco-Forero. Instance segmentation of 3D woven fabric from tomography images by Mathematical Morphology and Deep Learning methods. 2021. ⟨hal-03345132⟩

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