基于小波特征融合深度学习模型的钛合金焊缝飞行时间衍射图像去噪技术

IF 2.6 3区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
Zelin Zhi, Hongquan Jiang, Deyan Yang, Kun Yue, Jianmin Gao, Zhixiang Cheng, Yongjun Xu, Qiang Geng, Wei Zhou
{"title":"基于小波特征融合深度学习模型的钛合金焊缝飞行时间衍射图像去噪技术","authors":"Zelin Zhi,&nbsp;Hongquan Jiang,&nbsp;Deyan Yang,&nbsp;Kun Yue,&nbsp;Jianmin Gao,&nbsp;Zhixiang Cheng,&nbsp;Yongjun Xu,&nbsp;Qiang Geng,&nbsp;Wei Zhou","doi":"10.1007/s10921-024-01099-0","DOIUrl":null,"url":null,"abstract":"<div><p>Images of titanium alloy welds detected by time-of-flight diffraction (TOFD) have problems, including large noise signals and many interference streaks around the defects, all of which seriously limit the accuracy and effectiveness of defect recognition. Existing image denoising methods lack the knowledge of the noise characteristics of TOFD images of titanium alloy weld and the preprocessing experience of technicians in the field. In addition, it is difficult to select the parameters of the preprocessing methods, and they are easily influenced by the level of technical personnel, resulting in low efficiency and poor consistency in preprocessing. To address these problems, we proposed a denoising method based on the combination of wavelet band features and deep-learning theory for TOFD images of titanium alloy weld. First, based on the wavelet preprocessing method and the experience of nondestructive testing (NDT) technicians, we constructed an image pair dataset consisting of the original TOFD images of titanium alloy weld and the desired target images to realize the accumulation of engineers’ preprocessing knowledge. Second, we constructed a multiband wavelet feature fusion U-net image denoising model (WU-net) and designed a loss function under three constraints of image consistency, image texture information consistency, and structural similarity. This model was able to learn to achieve end-to-end adaptive denoising for TOFD images of titanium alloy weld. Third, we illustrated and validated the effectiveness of TOFD image preprocessing for titanium alloy weld. The results showed that the proposed method effectively eliminated TOFD image noise and improved the accuracy of defect recognition.</p></div>","PeriodicalId":655,"journal":{"name":"Journal of Nondestructive Evaluation","volume":"43 3","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Titanium Alloy Weld Time-of-Flight Diffraction Image Denoising Based on a Wavelet Feature Fusion Deep-Learning Model\",\"authors\":\"Zelin Zhi,&nbsp;Hongquan Jiang,&nbsp;Deyan Yang,&nbsp;Kun Yue,&nbsp;Jianmin Gao,&nbsp;Zhixiang Cheng,&nbsp;Yongjun Xu,&nbsp;Qiang Geng,&nbsp;Wei Zhou\",\"doi\":\"10.1007/s10921-024-01099-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Images of titanium alloy welds detected by time-of-flight diffraction (TOFD) have problems, including large noise signals and many interference streaks around the defects, all of which seriously limit the accuracy and effectiveness of defect recognition. Existing image denoising methods lack the knowledge of the noise characteristics of TOFD images of titanium alloy weld and the preprocessing experience of technicians in the field. In addition, it is difficult to select the parameters of the preprocessing methods, and they are easily influenced by the level of technical personnel, resulting in low efficiency and poor consistency in preprocessing. To address these problems, we proposed a denoising method based on the combination of wavelet band features and deep-learning theory for TOFD images of titanium alloy weld. First, based on the wavelet preprocessing method and the experience of nondestructive testing (NDT) technicians, we constructed an image pair dataset consisting of the original TOFD images of titanium alloy weld and the desired target images to realize the accumulation of engineers’ preprocessing knowledge. Second, we constructed a multiband wavelet feature fusion U-net image denoising model (WU-net) and designed a loss function under three constraints of image consistency, image texture information consistency, and structural similarity. This model was able to learn to achieve end-to-end adaptive denoising for TOFD images of titanium alloy weld. Third, we illustrated and validated the effectiveness of TOFD image preprocessing for titanium alloy weld. The results showed that the proposed method effectively eliminated TOFD image noise and improved the accuracy of defect recognition.</p></div>\",\"PeriodicalId\":655,\"journal\":{\"name\":\"Journal of Nondestructive Evaluation\",\"volume\":\"43 3\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nondestructive Evaluation\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10921-024-01099-0\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nondestructive Evaluation","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s10921-024-01099-0","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

摘要

通过飞行时间衍射(TOFD)检测到的钛合金焊缝图像存在噪声信号大、缺陷周围干扰条纹多等问题,这些都严重限制了缺陷识别的准确性和有效性。现有的图像去噪方法缺乏对钛合金焊缝 TOFD 图像噪声特征的了解和现场技术人员的预处理经验。此外,预处理方法的参数选择困难,容易受技术人员水平的影响,导致预处理效率低、一致性差。针对这些问题,我们提出了一种基于小波段特征与深度学习理论相结合的钛合金焊缝 TOFD 图像去噪方法。首先,基于小波预处理方法和无损检测(NDT)技术人员的经验,我们构建了由钛合金焊缝原始 TOFD 图像和所需目标图像组成的图像对数据集,实现了工程师预处理知识的积累。其次,我们构建了多波段小波特征融合 U-net 图像去噪模型(WU-net),并在图像一致性、图像纹理信息一致性和结构相似性三个约束条件下设计了损失函数。该模型能够通过学习实现钛合金焊缝 TOFD 图像的端到端自适应去噪。第三,我们说明并验证了钛合金焊缝 TOFD 图像预处理的有效性。结果表明,所提出的方法有效消除了 TOFD 图像噪声,提高了缺陷识别的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Titanium Alloy Weld Time-of-Flight Diffraction Image Denoising Based on a Wavelet Feature Fusion Deep-Learning Model

Titanium Alloy Weld Time-of-Flight Diffraction Image Denoising Based on a Wavelet Feature Fusion Deep-Learning Model

Titanium Alloy Weld Time-of-Flight Diffraction Image Denoising Based on a Wavelet Feature Fusion Deep-Learning Model

Images of titanium alloy welds detected by time-of-flight diffraction (TOFD) have problems, including large noise signals and many interference streaks around the defects, all of which seriously limit the accuracy and effectiveness of defect recognition. Existing image denoising methods lack the knowledge of the noise characteristics of TOFD images of titanium alloy weld and the preprocessing experience of technicians in the field. In addition, it is difficult to select the parameters of the preprocessing methods, and they are easily influenced by the level of technical personnel, resulting in low efficiency and poor consistency in preprocessing. To address these problems, we proposed a denoising method based on the combination of wavelet band features and deep-learning theory for TOFD images of titanium alloy weld. First, based on the wavelet preprocessing method and the experience of nondestructive testing (NDT) technicians, we constructed an image pair dataset consisting of the original TOFD images of titanium alloy weld and the desired target images to realize the accumulation of engineers’ preprocessing knowledge. Second, we constructed a multiband wavelet feature fusion U-net image denoising model (WU-net) and designed a loss function under three constraints of image consistency, image texture information consistency, and structural similarity. This model was able to learn to achieve end-to-end adaptive denoising for TOFD images of titanium alloy weld. Third, we illustrated and validated the effectiveness of TOFD image preprocessing for titanium alloy weld. The results showed that the proposed method effectively eliminated TOFD image noise and improved the accuracy of defect recognition.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Nondestructive Evaluation
Journal of Nondestructive Evaluation 工程技术-材料科学:表征与测试
CiteScore
4.90
自引率
7.10%
发文量
67
审稿时长
9 months
期刊介绍: Journal of Nondestructive Evaluation provides a forum for the broad range of scientific and engineering activities involved in developing a quantitative nondestructive evaluation (NDE) capability. This interdisciplinary journal publishes papers on the development of new equipment, analyses, and approaches to nondestructive measurements.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信