Enhanced Infrared Image Processing for Impacted Carbon/Glass Fiber-Reinforced Composite Evaluation

Zhang, Hai; Avdelidis, Nicolas P.; Osman, Ahmad; Ibarra‐Castanedo, Clemente; Сфарра, Стефано; Fernandes, Henrique; Matikas, Theodore E.; Maldague, Xavier

doi:10.3390/s18010045

Cited by 20 publications

(14 citation statements)

References 25 publications

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“…SNR is commonly used for validation among the scientific community, especially in NDT IR thermography work. Zhang [22] used SNR to validate results from a pre-processing proposed modality IRT method. The pre-processed method is different from the usual signal smoothing modality which only uses a polynomial fitting as the pre-processing method, whereas the proposed modality used the low-order derivatives to pre-process the raw thermal data alongside using advanced post-processing techniques such as PCT and PPT.…”

Section: Discussionmentioning

confidence: 99%

“…The temperature decays can be fitted with a functional relationship. Shepard et al [21] proposed such fitting several years ago; it is known as the TSR (thermographic signal reconstruction) algorithm, with which thermal data from pulsed thermography experiments are fitted using a logarithmic polynomial function [22]. This method was described well by Duan [20] for automated defect classification in infrared thermography based on a neural network [20].…”

Section: Defect Analysismentioning

confidence: 99%

“…The effectiveness of this modality was verified for PT. It was found that the method provided better performance than lock-in heating in PT, in which the presented pre-processing modality cannot be used because the thermal curves for lock-in heating are not linear [22].…”

Section: (C)mentioning

confidence: 99%

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Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites

Deane

Avdelidis

Ibarra-Castanedo

et al. 2020

Sensors

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This work aims to address the effectiveness and challenges of non-destructive testing (NDT) by active infrared thermography (IRT) for the inspection of aerospace-grade composite samples and seeks to compare uncooled and cooled thermal cameras using the signal-to-noise ratio (SNR) as a performance parameter. It focuses on locating impact damages and optimising the results using several signal processing techniques. The work successfully compares both types of cameras using seven different SNR definitions, to understand if a lower-resolution uncooled IR camera can achieve an acceptable NDT standard. Due to most uncooled cameras being small, lightweight, and cheap, they are more accessible to use on an unmanned aerial vehicle (UAV). The concept of using a UAV for NDT on a composite wing is explored, and the UAV is also tracked using a localisation system to observe the exact movement in millimetres and how it affects the thermal data. It was observed that an NDT UAV can access difficult areas and, therefore, can be suggested for significant reduction of time and cost.

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Section: Discussionmentioning

confidence: 99%

Section: Defect Analysismentioning

confidence: 99%

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Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites

Deane

Avdelidis

Ibarra-Castanedo

et al. 2020

Sensors

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“…In PT, a subsurface defect causes a non-uniform cooling, which can be identified in an imaging sequence. As a time-domain sequenced imaging approach, PT allows the application of the advanced imaging processing techniques to obtain more visible imprints of the defects [2]. Figure 2 shows the schematic configuration for PT.…”

Section: Infrared Thermographymentioning

confidence: 99%

Infrared and Terahertz time-domain imaging for evaluation of impacted thick homogeneous particleboards of sugarcane bagasse

Zhang¹,

Сфарра²,

Osman³

et al. 2018

Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography

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In this paper, both infrared thermography (IRT) and terahertz time-domain imaging (THz-TDI) were used to evaluate impacted homogeneous particleboards of sugarcane bagasse (SCB), which have the thickness of 2 cm. In this study, the THz data were processed by applying the well-known image processing algorithms used in IRT. The comparative results show that IRT can provide some finer details in the detection of shallow thermal imprints; however, it is difficult to reveal deeper (typically ~1 cm) information. On the contrary, THz-TDI may explore the entire thickness of the samples, although -for this specific study -its resolution is lower. In addition, THz-TDI has the advantage to detect resin-rich and fibre-rich regions..

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“…Then, a total variation regularization term is implemented on the occlusion matrix in order to avoid the sensor noise being fitted into the occlusion term. To solve the afore-proposed convex optimization problem, principal component analysis (PCA) [36,37] and the accelerated proximal gradient (APG) [38] methods are combined as an alternating algorithm to formulate a two-step optimization. Finally, the target location is calculated under the PF framework along with a dynamic model update mechanism.…”

Section: Introductionmentioning

confidence: 99%

Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking

Wan

Qian

et al. 2018

Remote Sensing

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Infrared moving target tracking plays a fundamental role in many burgeoning research areas of Smart City. Challenges in developing a suitable tracker for infrared images are particularly caused by pose variation, occlusion, and noise. In order to overcome these adverse interferences, a total variation regularization term-based low-rank and sparse matrix representation (TV-LRSMR) model is designed in order to exploit a robust infrared moving target tracker in this paper. First of all, the observation matrix that is derived from the infrared sequence is decomposed into a low-rank target matrix and a sparse occlusion matrix. For the purpose of preventing the noise pixel from being separated into the occlusion term, a total variation regularization term is proposed to further constrain the occlusion matrix. Then an alternating algorithm combing principal component analysis and accelerated proximal gradient methods is employed to separately optimize the two matrices. For long-term tracking, the presented algorithm is implemented using a Bayesien state inference under the particle filtering framework along with a dynamic model update mechanism. Both qualitative and quantitative experiments that were examined on real infrared video sequences verify that our algorithm outperforms other state-of-the-art methods in terms of precision rate and success rate.

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Enhanced Infrared Image Processing for Impacted Carbon/Glass Fiber-Reinforced Composite Evaluation

Cited by 20 publications

References 25 publications

Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites

Comparison of Cooled and Uncooled IR Sensors by Means of Signal-to-Noise Ratio for NDT Diagnostics of Aerospace Grade Composites

Infrared and Terahertz time-domain imaging for evaluation of impacted thick homogeneous particleboards of sugarcane bagasse

Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking

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