<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ME</journal-id><journal-title-group><journal-title>Modern Engineering</journal-title></journal-title-group><issn>2996-6973</issn><eissn>2996-6981</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ME.2026040002</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于合成散斑技术和深度学习的DIC位移测试技术研究</title><url>https://artdesignp.com/journal/ME/3/4/10.61369/ME.2026040002</url><author>卢烁,陈佳妍,唐锦辉</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>3</volume><issue>4</issue><history><date date-type="pub"><published-time>2026-04-20</published-time></date></history><abstract>数字图像相关（Digital Image Correlation, DIC）是一种非接触式全场光学测量技术，能够获取材料和结构在加载过程中的位移信息，已广泛应用于材料力学试验与结构健康监测等领域。传统DIC方法依赖子集匹配与迭代优化算法计算位移场，计算量大、处理时间长。为解决上述不足，本文开展基于深度学习的DIC位移预测方法研究。该技术为材料试验、结构性能评估以及工业在线监测提供了一种高效、可靠的全场位移测量新方法。</abstract><keywords>数字图像相关（DIC）,深度学习,位移预测,光流法,水转印散斑,Transformer</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]Boukhtache S, Abdelouahab K, Berry F, et al. When Deep Learning Meets Digital Image Correlation [J]. Optics and Lasers in Engineering, 2021, 136: 106308.[2]Sur F, Blaysat B, Gr&amp;eacute;diac M. On biases in displacement estimation for image registration, with a focus on photomechanics [J]. Journal of Mathematical Imaging and Vision, 2021, 63(7): 777-806.[3]Wang B, Pan B. Subset-based local vs. finite element-based global digital image correlation: A comparison study [J]. Theoretical and Applied Mechanics Letters, 2016, 6(5): 200-8.[4]Duan X, Xu H, Dong R, et al. Digital image correlation based on convolutional neural networks [J]. Optics and Lasers in Engineering, 2023, 160: 107234.[5] 申海艇, 蒋招绣, 王贝壳, 等. 基于超高速相机的数字图像相关性全场应变分析在SHTB实验中的应用[J].爆炸与冲击,2017,37(1):15-20.[6] 潘兵, 谢惠民. 数字图像相关中基于位移场局部最小二乘拟合的全场应变测量[J].光学学报,2007,(11):1980-1986.[7]潘兵, 吴大方, 夏勇. 数字图像相关方法中散斑图的质量评价研究[J].实验力学,2010,25(2):120-129.[8] 陈佳, 胡浩博, 何儒汉, 等. 基于注意力机制的增强特征描述子 [J]. 计算机工程, 2021, 47(5): 260-6.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
