Identification of Carbon-Short Fibers by Image Segmentation Technologies

Evgenii Kurkin; Evgenii Minaev; Andrey Sedelnikov; Jose Gabriel Quijada Pioquinto; Vladislava Chertykovtseva; Andrey Gavrilov

doi:10.20944/preprints202410.0160.v1

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preprints.org > chemistry and materials science > materials science and technology > doi: 10.20944/preprints202410.0160.v1

Preprint Article Version 1 This version is not peer-reviewed

Identification of Carbon-Short Fibers by Image Segmentation Technologies

Evgenii Kurkin

Evgenii Minaev

Andrey Sedelnikov

^* ,

Jose Gabriel Quijada Pioquinto

Vladislava Chertykovtseva

Andrey Gavrilov

Version 1 : Received: 1 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (12:07:03 CEST)

How to cite: Kurkin, E.; Minaev, E.; Sedelnikov, A.; Pioquinto, J. G. Q.; Chertykovtseva, V.; Gavrilov, A. Identification of Carbon-Short Fibers by Image Segmentation Technologies. Preprints 2024, 2024100160. https://doi.org/10.20944/preprints202410.0160.v1 Kurkin, E.; Minaev, E.; Sedelnikov, A.; Pioquinto, J. G. Q.; Chertykovtseva, V.; Gavrilov, A. Identification of Carbon-Short Fibers by Image Segmentation Technologies. Preprints 2024, 2024100160. https://doi.org/10.20944/preprints202410.0160.v1

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MDPI and ACS Style

Kurkin, E.; Minaev, E.; Sedelnikov, A.; Pioquinto, J. G. Q.; Chertykovtseva, V.; Gavrilov, A. Identification of Carbon-Short Fibers by Image Segmentation Technologies. Preprints 2024, 2024100160. https://doi.org/10.20944/preprints202410.0160.v1

APA Style

Kurkin, E., Minaev, E., Sedelnikov, A., Pioquinto, J. G. Q., Chertykovtseva, V., & Gavrilov, A. (2024). Identification of Carbon-Short Fibers by Image Segmentation Technologies. Preprints. https://doi.org/10.20944/preprints202410.0160.v1

Chicago/Turabian Style

Kurkin, E., Vladislava Chertykovtseva and Andrey Gavrilov. 2024 "Identification of Carbon-Short Fibers by Image Segmentation Technologies" Preprints. https://doi.org/10.20944/preprints202410.0160.v1

Abstract

Computer vision technology for automatic recognition and geometric characterization of carbon fibers was proposed in this paper. A two-stage pipeline was used, in the first stage the fundamental neural network model of segmentation SAM(Segment Anything Model) was used, in the second stage the segmentation results were improved by using a trained neural network model DeepLabv3+. Both manually labeled carbon fiber images and generated synthetic images were used for training. It is shown that additional use of synthetic data in training of neural network models improves the quality of segmentation by IoU and Pix Acc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e. by 1% on average. The total processing time averaged 0.95 seconds for a single fiber image. Based on the segmentation results, an automatic calculation of geometric characteristics and statistics that can be used to evaluate material properties was realized.

Keywords

carbon short-fibers; virtual training; image segmentation; instance segmentation; semantic segmentation; DeepLabv3+; SAM; Hough technique

Subject

Chemistry and Materials Science, Materials Science and Technology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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