Metrological characterization and  calibration of thermographic cameras for  quantitative temperature measurement

König, S.; Gutschwager, B.; Taubert, R.; Hollandt, J.

doi:10.5194/jsss-9-425-2020

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…There are well-known studies on the uncertainty of thermographic temperature measurement [23][24][25]. The B-type uncertainty method has also been applied for thermographic measurement temperature uncertainty determination [26,27]. The authors did not find any articles on the inclusion in the uncertainty budget of the factor related to the unsharpness of the registered thermogram.…”

Section: Introductionmentioning

confidence: 99%

Lack of Thermogram Sharpness as Component of Thermographic Temperature Measurement Uncertainty Budget

Dziarski

Hulewicz

Dombek

2021

Sensors

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The number of components of a thermographic temperature measurement uncertainty budget and their ultimate contribution depend on the conditions in which the measurement is performed. The acquired data determine the accuracy with which the uncertainty component is estimated. Unfortunately, when some factors have to be taken into account, it is difficult to determine the value of the uncertainty component caused by the occurrence of this factor. In the case of a thermographic temperature measurement, such a factor is the lack of sharpness of the registered thermogram. This problem intensifies when an additional macro lens must be used. Therefore, it is decided to commence research to prepare an uncertainty budget of thermographic measurement with an additional macro lens based on the B method described in EA-4/02 (European Accreditation publications). As a result, the contribution of factors in the uncertainty budget of thermographic measurement with additional macro lens and the value of expanded uncertainty were obtained.

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Section: Introductionmentioning

confidence: 99%

Lack of Thermogram Sharpness as Component of Thermographic Temperature Measurement Uncertainty Budget

Dziarski

Hulewicz

Dombek

2021

Sensors

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“…Established protocols for calibration of non-contact temperature measuring instruments, and consequently also most scientific publications in this field, mostly refer to calibrations of radiation thermometers which measure temperature of the black body. Such a calibration protocol is described by Murthy, Tsai and Saunders [1], König et al [2], Donlon et al [3], Boles [4], Kim et al [5] and Miklavec et al [6].…”

Section: Radiation Thermometry Of Black Bodiesmentioning

confidence: 99%

“…A general form of interaction of radiation with the medium is described by equation (2), where 𝑀𝑀 bb (𝜆𝜆, 𝑇𝑇) represents object's blackbody emission, 𝑀𝑀 inc. (𝜆𝜆) represents incident radiation, which is reflected from the surface of a medium, 𝑀𝑀 beh. (𝜆𝜆, 𝑇𝑇) represents spectral radiance behind the object in the case of transmission through a translucent material and 𝑀𝑀 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 (𝜆𝜆, 𝑇𝑇) represents the radiation from the object after interaction, also called object radiation, consisting of all three radiant contributions,…”

Section: Thermal Radiation Emission Transfer and Absorptionmentioning

confidence: 99%

Traceable Spectral Radiation Model of Radiation Thermometry

Mlačnik¹,

Pušnik²

2023

Preprint

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Despite great technical capabilities, the theory of non-contact temperature measurement is usually not fully applicable to the use of measuring instruments in practice. While black body calibrations are in practice well established and easy to accomplish, this calibration protocol is never fully applicable to measurements of real objects under real conditions. Currently, the best approximation to real world radiation thermometry is grey body radiation thermometry, which is supported by most measuring instruments to date. Nevertheless, the metrological requirements necessitate traceability, therefore real body radiation thermometry is required for temperature measurements of real bodies. This article documents the current state of temperature calculation algorithms for radiation-thermometers and the creation of a traceable model for radiation thermometry of real bodies that uses a digital twin model of the system of measurement to compensate for the loss of data, caused by spectral integration, which occurs when thermal radiation is absorbed on the active surface of the sensor. To solve this problem, a hybrid model with variable scalar inputs is proposed, in which the scalar input parameters are calculated to correspond to the system of measurement. The method for calculating the effective parameters is proposed and verified with the theoretical model of non-contact thermometry. The sum of effective instrumental parameters is presented for different temperatures to show that the rule of radiation thermometry of grey bodies, according to which the sum of instrumental emissivity and instrumental reflectivity equals to 1, does not hold in radiation thermometry for real bodies. Using the derived models of radiation thermometry, the uncertainty of radiation thermometry due to the uncertainty of spectral emissivity is analysed by simulated measurements along the temperature ranges of various radiation thermometers, enabling traceable non-contact temperature measurements with known measurement uncertainty under any known conditions.

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“…The correction value from the thermal camera is obtained by calibrating it. This calibration process is performed using a radiometric calibration method, which establishes the relationship between the pixel signal and the temperature of the target object [ 38 , 39 ]. Calibrated thermal cameras minimize temperature readings that differ significantly from reference devices and become more reliable for medical applications.…”

Section: System Architecture In Generalmentioning

confidence: 99%

Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review

Manullang

Lin

Lai

et al. 2021

Sensors

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Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.

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Metrological characterization and calibration of thermographic cameras for quantitative temperature measurement

Cited by 8 publications

References 14 publications

Lack of Thermogram Sharpness as Component of Thermographic Temperature Measurement Uncertainty Budget

Lack of Thermogram Sharpness as Component of Thermographic Temperature Measurement Uncertainty Budget

Traceable Spectral Radiation Model of Radiation Thermometry

Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review

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