A new signal processing algorithm of pulsed infrared thermography

Tang, Qingju; Bu, Chiwu; Liu, Yuanlin; Lei, Qi; Yu, Zhou

doi:10.1016/j.infrared.2014.12.002

Cited by 22 publications

(4 citation statements)

References 13 publications

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“…The CCIPCT method is an extension of Principal Component Thermography (PCT) which has been proposed as a method for defect depth characterization [23,24]. The other relatively new PCA application consists in the enhancement of contrast [25] and removal of the noise and information redundancy of the infrared image sequences [26].…”

Section: Application Of Pca To Infrared Image Analysis In Production mentioning

confidence: 99%

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Multivariate Analysis of Transient State Infrared Images in Production Line Quality Control Systems

Cristalli

Grabowski

2018

Applied Sciences

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Manufacturers would like to increase production volumes while preserving the high quality of their products. The long testing times can cause a bottleneck of production processes especially taking into account the observed tendency for testing all produced devices. The main aim of this work consists in the analysis of time changes of features extracted from thermal images using the multivariate approach. The paper shows that if the principal component analysis (PCA), belonging to multivariate methods, is applied for quality control based on infrared images, then the minimum testing times can be estimated. In order to draw the final conclusions regarding testing times and, what is also very important, which principal components should be selected for classification, a detailed temporal analysis for an exemplary production line has been carried out. The future impact of the results includes higher productivity and cost-effectiveness due to the determination of an optimal decision time in production line quality control systems using the proposed approach.

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Section: Application Of Pca To Infrared Image Analysis In Production mentioning

confidence: 99%

“…The other relatively new PCA application consists in the enhancement of contrast [25] and removal of the noise and information redundancy of the infrared image sequences [26]. The transformation of the original thermal images into principal components applied in production line quality control has been illustrated in Figure 1.…”

Section: Appl Sci 2018 8 Xmentioning

confidence: 99%

Multivariate Analysis of Transient State Infrared Images in Production Line Quality Control Systems

Cristalli

Grabowski

2018

Applied Sciences

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“…Each of these techniques maintains its specific advancements and constraints. Pulse thermography (PT) [6][7][8] has a high-power source (peak) with a short period deploys through a flash lamp along with a response of temperature effect being observed from a test sample surface. This recorded thermal response is utilized for the analysis of subsurface features.…”

Section: Introductionmentioning

confidence: 99%

Deep Learnıng-Based Sustaınable Subsurface Anomalıes Detectıon In Barker-Coded Thermal Wave Imagıng

Parvez¹,

Mohammad²,

Ghali³

et al. 2023

Preprint

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Deep learning-based sustainable subsurface anomaly detection is the perceiving of thermographic research. Subsurface detection of an anomaly in various materials using deep learning increases reliability. This article aims to describe a method that uses thermal wave imaging to identify subsurface anomalies in materials. The proposed method is based on the experiments that were carried out with different kinds of samples and have been compared to other modern techniques for detecting subsurface anomalies. Subsurface anomalies visualized using the proposed deep learning method give better visualization, and the results were compared to that of contemporary approaches. In addition, region-based active contour segmentation-based detection is also proposed for the GFRP sample.

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“…As for the noise removal, R. Usamentiaga et al [8] utilized the short Fourier transform to process the thermal image and improved the signal-to-noise ratio (SNR). Q. J. Tang et al [9] proposed a method by combining the Markov chain with principal component analysis to perform an excellent capability to improve SNR. Moreover, several methods including independence component analysis [10], wavelet decomposition [11], mathematical morphology [12], etc.…”

Section: Introductionmentioning

confidence: 99%

Inspection Detectability Improvement for Metal Defects Detected by Pulsed Infrared Thermography

Yang

Kou

et al. 2019

Photonic Sens

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Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thermography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.

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A new signal processing algorithm of pulsed infrared thermography

Cited by 22 publications

References 13 publications

Multivariate Analysis of Transient State Infrared Images in Production Line Quality Control Systems

Multivariate Analysis of Transient State Infrared Images in Production Line Quality Control Systems

Deep Learnıng-Based Sustaınable Subsurface Anomalıes Detectıon In Barker-Coded Thermal Wave Imagıng

Inspection Detectability Improvement for Metal Defects Detected by Pulsed Infrared Thermography

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