用于碳纤维增强聚合物检测的脉冲热成像数据的潜在低阶表示

IF 3.7 3区工程技术 Q1 INSTRUMENTS & INSTRUMENTATION

Quantitative Infrared Thermography Journal Pub Date : 2022-05-17 DOI:10.1080/17686733.2022.2047301

J. Fleuret, S. Ebrahimi, C. Ibarra-Castanedo, X. Maldague

{"title":"用于碳纤维增强聚合物检测的脉冲热成像数据的潜在低阶表示","authors":"J. Fleuret, S. Ebrahimi, C. Ibarra-Castanedo, X. Maldague","doi":"10.1080/17686733.2022.2047301","DOIUrl":null,"url":null,"abstract":"ABSTRACT This paper explores the implementation of Latent Low-Rank Representation (LatLRR) on pulsed thermographic data. LatLRR decomposes an image in the form of a linear association of three types of information: observed, unobserved and noise. This information is then used in order to separate the salient and principal features. This study has found that when used as a post-processing method prior to the application of state-of-the-art signal processing techniques, such as principal component thermography (PCT) and pulsed phase thermography (PPT), LatLRR significantly improves defect detection: 18% for PCT and 92% for PPT. Nevertheless, no noticeable improvement was measured when LatLRR was used to reconstruct a noiseless version of each image of a dataset, before processing it with a state-of-the-art algorithm. The investigations conducted on each type of feature returned by the LatLRR have also failed to provide results regarding the detection of defects.","PeriodicalId":54525,"journal":{"name":"Quantitative Infrared Thermography Journal","volume":"20 1","pages":"143 - 156"},"PeriodicalIF":3.7000,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Latent Low Rank Representation Applied to Pulsed Thermography Data For Carbon Fibre Reinforced Polymer Inspection\",\"authors\":\"J. Fleuret, S. Ebrahimi, C. Ibarra-Castanedo, X. Maldague\",\"doi\":\"10.1080/17686733.2022.2047301\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT This paper explores the implementation of Latent Low-Rank Representation (LatLRR) on pulsed thermographic data. LatLRR decomposes an image in the form of a linear association of three types of information: observed, unobserved and noise. This information is then used in order to separate the salient and principal features. This study has found that when used as a post-processing method prior to the application of state-of-the-art signal processing techniques, such as principal component thermography (PCT) and pulsed phase thermography (PPT), LatLRR significantly improves defect detection: 18% for PCT and 92% for PPT. Nevertheless, no noticeable improvement was measured when LatLRR was used to reconstruct a noiseless version of each image of a dataset, before processing it with a state-of-the-art algorithm. The investigations conducted on each type of feature returned by the LatLRR have also failed to provide results regarding the detection of defects.\",\"PeriodicalId\":54525,\"journal\":{\"name\":\"Quantitative Infrared Thermography Journal\",\"volume\":\"20 1\",\"pages\":\"143 - 156\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2022-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative Infrared Thermography Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/17686733.2022.2047301\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Infrared Thermography Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/17686733.2022.2047301","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}

引用次数: 1

摘要

摘要本文探讨了潜在低秩表示（LatLRR）在脉冲热成像数据上的实现。LatLRR以三种类型的信息的线性关联形式分解图像：观察到的、未观察到的和噪声。然后使用这些信息来区分显著特征和主要特征。本研究发现，在应用最先进的信号处理技术（如主成分热成像（PCT）和脉冲相位热成像（PPT））之前，将LatLRR用作后处理方法时，LatLRR显著提高了缺陷检测：PCT为18%，PPT为92%。然而，在用最先进的算法处理之前，使用LatLRR重建数据集的每个图像的无噪声版本时，没有测量到明显的改进。对LatLRR返回的每种类型的特征进行的调查也未能提供有关缺陷检测的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Latent Low Rank Representation Applied to Pulsed Thermography Data For Carbon Fibre Reinforced Polymer Inspection

ABSTRACT This paper explores the implementation of Latent Low-Rank Representation (LatLRR) on pulsed thermographic data. LatLRR decomposes an image in the form of a linear association of three types of information: observed, unobserved and noise. This information is then used in order to separate the salient and principal features. This study has found that when used as a post-processing method prior to the application of state-of-the-art signal processing techniques, such as principal component thermography (PCT) and pulsed phase thermography (PPT), LatLRR significantly improves defect detection: 18% for PCT and 92% for PPT. Nevertheless, no noticeable improvement was measured when LatLRR was used to reconstruct a noiseless version of each image of a dataset, before processing it with a state-of-the-art algorithm. The investigations conducted on each type of feature returned by the LatLRR have also failed to provide results regarding the detection of defects.

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

Quantitative Infrared Thermography Journal Physics and Astronomy-Instrumentation

CiteScore

6.80

自引率

12.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Quantitative InfraRed Thermography Journal (QIRT) provides a forum for industry and academia to discuss the latest developments of instrumentation, theoretical and experimental practices, data reduction, and image processing related to infrared thermography.