A comparison of image processing algorithms for thermal nondestructive evaluation

Zalameda, Joseph N.; Rajic, Nik; Winfree, William P.

doi:10.1117/12.485869

Cited by 32 publications

(24 citation statements)

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“…Each of these methods has advantages for specific tasks. Several methods address the challenge of getting a higher image quality; therefore image processing/analysis have become a central issue within almost all thermal non-destructive testing (TNDT) problems [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Thermal non-destructive testing: Localization of hidden defects

Hatefipour¹,

Ahlberg²,

Wren³

et al. 2014

Proceedings of the 2014 International Conference on Quantitative InfraRed Thermography

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During the last decades, a substantial amount of research and practical work has been conducted on non-destructive testing of materials using thermography. The performed studies elucidate the potential of various types of thermal non-destructive testing (TNDT) for different materials and applications, including various types of defects. This paper presents a method for detecting in-depth defects in metallic materials and a simulation model for the heat transfer in the material. Experiments are performed on a test specimen with artificial defects (flatbottom holes). The detection method exploits spatiotemporal analysis in order to find deviations from a model of normality, and shows novel results. Thermal modelling is performed in order to have a base-line simulation model enable us to (a) investigate affecting parameters without repeating the experiments and (b) generalize the results and extend their validity to other cases. Results show that there is an acceptable compliance between simulated and measured thermal data.

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Section: Introductionmentioning

confidence: 99%

Thermal non-destructive testing: Localization of hidden defects

Hatefipour¹,

Ahlberg²,

Wren³

et al. 2014

Proceedings of the 2014 International Conference on Quantitative InfraRed Thermography

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“…where σ is the standard deviation of the noise and Sarea mean and Narea mean are the average values of each area, respectively [13]. The automatic quantification method, which is based on signal to noise ratio analysis applied to the processing techniques of the images mentioned of the previous sections, is independent of how the defect appears in the material.…”

Section: Signal To Noise Ratiomentioning

confidence: 99%

“…However, adapting and modifying the algorithms, they can be applied to the other existing techniques. These techniques can be mainly classified in three groups: techniques using thermal contrast [6], techniques based on transforms [3] and techniques using statistical methods [7,8]. Some of the main techniques of these groups will be explained below.…”

Section: Processing Techniquesmentioning

confidence: 99%

“…This paper is focused on the automation of the comparative study of the techniques processing thermographic sequences obtained in non-destructive testing using active infrared thermography. This automation process is based on the quantification of goodness through the signal to noise ratio (SNR) of some of the main processing techniques (DAC, PPT, PCT, TSR, PhAC, Skewness and Kurtosis) employed in thermal sequence analysis [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Quantification by Signal to Noise Ratio of Active Infrared Thermography Data Processing Techniques

Hidalgo-Gato¹,

Andrés²,

López‐Higuera³

et al. 2013

OPJ

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In this paper, the use of a signal to noise ratio (SNR) is proposed for the quantification of the goodness of some selected processing techniques of thermographic images, such as differentiated absolute contrast, skewness and kurtosis based algorithms, pulsed phase transform, principal component analysis and thermographic signal reconstruction. A new hybrid technique is also applied (PhAC-Phase absolute contrast), it combines three different processing techniques: phase absolute contrast, pulsed phase thermography and thermographic signal reconstruction. The quality of the results is established on the basis of the values of the parameter SNR, assessed for the present defects in the analyzed specimen, which enabled to quantify and compare their identification and the quality of the results of the employed technique.

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“…However, some of these routines as thermal signal reconstruction (TSR) [6] requires a priori knowledge of the defectives depth and their lateral dimensions, in other words their aspect ratio (depth/lateral dimension), to provide accurate depth predictions. Additionally, other routines such as pulse phase thermography PPT [7] provide inconsistent predictions when varying the sampling rate and/or the experiment duration [8,9]. So, in this application we utilize a novel processing routine named "self-referencing" presented in [10], due to its insensitivity to implants aspect ratio and experiment variations.…”

Section: Data Processing and Correlationmentioning

confidence: 99%

Calibrated Pulse-Thermography Procedure for Inspecting HDPE

Omar

Zhou

Parvataneni

et al. 2008

Research Letters in Materials Science

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Recommended by Rui VilarThis manuscript discusses the application of a pulse-thermography modality to evaluate the integrity of a high-density polyethylene HDPE joint for delamination, in nonintrusive manner. The inspected HDPE structure is a twin-cup shape, molded through extrusion, and the inspection system comprises a high-intensity, short-duration radiation pulse to excite thermal emission; the text calibrates the experiment settings (pulse duration, and detector sampling rate) to accommodate HDPE bulks thermal response. The acquired thermal scans are processed through new contrast computation named "self-referencing", to investigate the joint tensile strength and further map its adhesion interface in real-time. The proposed system (hardware, software combination) performance is assessed through an ultrasound C-scan validation and further benchmarked using a standard pulse phase thermography (PPT) routine.

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A comparison of image processing algorithms for thermal nondestructive evaluation

Cited by 32 publications

References 0 publications

Thermal non-destructive testing: Localization of hidden defects

Thermal non-destructive testing: Localization of hidden defects

Quantification by Signal to Noise Ratio of Active Infrared Thermography Data Processing Techniques

Calibrated Pulse-Thermography Procedure for Inspecting HDPE

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