Hierarchical low-rank and sparse tensor micro defects decomposition by electromagnetic thermography imaging system

Wu, Tongle; Gao, Bin; Woo, Wai Lok

doi:10.1098/rsta.2019.0584

Cited by 12 publications

(3 citation statements)

References 47 publications

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“…The original thermal image in figure 5 is processed by the robust low-rank sparse tensor decomposition algorithm [14] , which effectively suppresses the effects of background and noise in the thermal images. The final results are displayed in figure 6.…”

Section: Detection Results Of Subsurface Defectsmentioning

confidence: 99%

Influence of water film on subsurface defect detection using eddy current pulsed thermography

Liu

Gao

et al. 2023

J. Phys.: Conf. Ser.

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Eddy current pulsed thermography (ECPT) is an effective non-destructive testing (NDT) technique for detecting flaws in metallic materials. However, due to the low radiation and high reflection properties of metallic materials, as well as the inhomogeneous emission caused by the complex states of the material surface, the detection of subsurface defects becomes difficult. In this paper, the physical mechanisms underlying the interference of the thin water film in ECPT detection are studied. Two static comparative experiments have been carried out on the ferromagnetic plate with several artificial subsurface defects. In the first comparative experiment, a high radiation point has been artificially added near the defects. The experimental results showed that water film could eliminate the influence of inhomogeneous emission. In the second experiments, the effectiveness of water film in improving the detectability of subsurface defects has been demonstrated and the subsurface defects with a maximum buried depth of 0.4 mm can be detected.

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Section: Detection Results Of Subsurface Defectsmentioning

confidence: 99%

Influence of water film on subsurface defect detection using eddy current pulsed thermography

Liu

Gao

et al. 2023

J. Phys.: Conf. Ser.

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“…Pattern decomposition methods [41] like nonnegative matrix factorization [42] and ensemble joint sparse low rank matrix decomposition [43] are examples of unsupervised learning strategies. Higher order decomposition approaches, such as low-rank tensor decomposition [44,45] and hierarchical sparse tensor decomposition [46] , can result in improved performance. This would be the future path of study to improve plastic waste classification.…”

Section: Discussionmentioning

confidence: 99%

Deep transfer learning benchmark for plastic waste classification

Chazhoor¹,

Ho²,

Gao³

et al. 2022

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Millions of people throughout the world have been harmed by plastic pollution. There are microscopic pieces of plastic in the food we eat, the water we drink, and even the air we breathe. Every year, the average human consumes 74,000 microplastics, which has a significant impact on their health. This pollution must be addressed before it has a significant negative influence on the population. This research benchmarks six state-of-the-art convolutional neural network models pre-trained on the ImageNet Dataset. The models Resnet-50, ResNeXt, MobileNet_v2, DenseNet, SchuffleNet and AlexNet were tested and evaluated on the WaDaBa plastic dataset, to classify plastic types based on their resin codes by integrating the power of transfer learning. The accuracy and training time for each model has been compared in this research. Due to the imbalance in the data, the under-sampling approach has been used. The ResNeXt model attains the highest accuracy in fourteen minutes.

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“…They improved the efficiency of the algorithm by optimizing the incremental multiplier parameter. Wu et al [22] proposed a novel hierarchical low-rank and sparse tensor decomposition method to detect anomalies in the induction thermography stream. This approach can suppress the interference of a strong background and sharpens the visual features of defects.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers

Ebrahimi

Fleuret

Klein

et al. 2021

Sensors

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Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.

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Hierarchical low-rank and sparse tensor micro defects decomposition by electromagnetic thermography imaging system

Cited by 12 publications

References 47 publications

Influence of water film on subsurface defect detection using eddy current pulsed thermography

Influence of water film on subsurface defect detection using eddy current pulsed thermography

Deep transfer learning benchmark for plastic waste classification

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers

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