Stress Fields Extraction in Multi-Polarized Photoelasticity Images Using Deep Convolutional Neural Networks

Diego Eusse Naranjo; Juan C. Briñez-De León; Juan C. Briñez-De León; Alejandro Restrepo-Martínez

doi:10.1364/3D.2022.JW2A.6

Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP)
Technical Digest Series (Optica Publishing Group, 2022),
paper JW2A.6
•https://doi.org/10.1364/3D.2022.JW2A.6

Stress Fields Extraction in Multi-Polarized Photoelasticity Images Using Deep Convolutional Neural Networks

Diego Eusse Naranjo, Juan C. Briñez-De León, and Alejandro Restrepo-Martínez

Propagation Through and Characterization of Atmospheric and Oceanic Phenomena 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-09-8

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Joint Poster Session II (Remote) (JW2A)

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D. E. Naranjo, J. C. Briñez-De León, and A. Restrepo-Martínez, "Stress Fields Extraction in Multi-Polarized Photoelasticity Images Using Deep Convolutional Neural Networks," in Imaging and Applied Optics Congress 2022 (3D, AOA, COSI, ISA, pcAOP), Technical Digest Series (Optica Publishing Group, 2022), paper JW2A.6.

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Abstract

Digital photoelasticity requires demodulating stress fields, wrapped into color fringe patterns. As an alternative to traditional methods, deep convolutional neural networks are trained to recover stress maps from isochromatic images related to different orientations of a polarized camera, reaching high precision in different analytical models.

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