Thermographic Data Analysis for Defect Detection by Imposing Spatial Connectivity and Sparsity Constraints in Principal Component Thermography

Wen, Ching-Mei; Сфарра, Стефано; Gargiulo, Giuliano; Yao, Yuan

doi:10.1109/tii.2020.3010273

Cited by 21 publications

(9 citation statements)

References 26 publications

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“…The locations of defects are shown in Figure 5 b, where the green boxes indicate the loss of surface decorations and the red box is a delamination defect. In [ 57 ], Wen et al developed an edge-group sparse PCT (ESPCT) method for the thermographic data analysis of the thermograms of this sample collected in a PT experiment. ESPCT can be regarded as an extension of PCT by imposing sparsity and spatial connectivity constraints.…”

Section: Pt And/or Passive Irt Applicationsmentioning

confidence: 99%

“…Figure 6 visualizes the results obtained by the deep learning model. Different from the methods used in [ 43 , 57 ], Mask R-CNN belongs to the family of supervised learning, which means a labeling step is necessary before the model training. In addition, the generalization performance of this model to other marquetry samples has not yet been tested.…”

Section: Pt And/or Passive Irt Applicationsmentioning

confidence: 99%

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Thermographic Imaging in Cultural Heritage: A Short Review

Dritsa

Orazi

Yao

et al. 2022

Sensors

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Over the recent period, there has been an increasing interest in the use of pulsed infrared thermography (PT) for the non-destructive evaluation of Cultural Heritage (CH). Unlike other techniques that are commonly employed in the same field, PT enables the depth-resolved detection of different kinds of subsurface features, thus providing helpful information for both scholars and restorers. Due to this reason, several research activities are currently underway to further improve the PT effectiveness. In this manuscript, the specific use of PT for the analysis of three different types of CH, namely documentary materials, panel paintings–marquetery, and mosaics, will be reviewed. In the latter case, i.e., mosaics, passive thermography combined with ground penetrating radar (GPR) and digital microscopy (DM) have also been deepened, considering their suitability in the open field. Such items have been selected because they are characterized by quite distinct physical and structural properties and, therefore, different PT (and, in some cases, verification) approaches have been employed for their investigations.

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Section: Pt And/or Passive Irt Applicationsmentioning

confidence: 99%

Section: Pt And/or Passive Irt Applicationsmentioning

confidence: 99%

Thermographic Imaging in Cultural Heritage: A Short Review

Dritsa

Orazi

Yao

et al. 2022

Sensors

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“…They showed that SPCT outperforms existing techniques with high computational performance. Wen et al [62,63] introduced an improved version of SPCT called Edge-Group Sparse Principal Component Thermography (ESPCT) that can preserve the spatial connectivity of thermal image pixels. They showed in experiments conducted on marquetry sample that ESPCT results provide higher contrast and SNR than PCT and SPCT.…”

Section: Advances Of Pctmentioning

confidence: 99%

Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

2021

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Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

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“…Wen et al [ 21 , 22 ] used an improved version of the Sparse-PCA to speed up processing. This method named Edge-Group Sparse PCA (ESPCT) [ 23 ] was significantly faster than SPCT, although still noticeably slower than PCT.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Principal Component Thermography for Defect Detection in Composites

Ebrahimi

Fleuret

Klein

et al. 2021

Sensors

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Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time.

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Thermographic Data Analysis for Defect Detection by Imposing Spatial Connectivity and Sparsity Constraints in Principal Component Thermography

Cited by 21 publications

References 26 publications

Thermographic Imaging in Cultural Heritage: A Short Review

Thermographic Imaging in Cultural Heritage: A Short Review

Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

Robust Principal Component Thermography for Defect Detection in Composites

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