基于深度卷积神经网络的多极化光弹性图像应力场提取

Diego Eusse Naranjo, J. B. Briñez-de León, Alejandro Restrepo-Martínez
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引用次数: 1

摘要

数字光弹性需要解调应力场,包裹成彩色条纹图案。作为传统方法的替代方案,训练深度卷积神经网络从偏振相机不同方向的等色图像中恢复应力图,在不同的分析模型中达到高精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stress Fields Extraction in Multi-Polarized Photoelasticity Images Using Deep Convolutional Neural Networks
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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