High-speed 3D thermography

Landmann, Martin; Heist, Stefan; Dietrich, Patrick; Lutzke, Peter; Gebhart, Ingo; Templin, Joachim; Kühmstedt, Peter; Tünnermann, Andreas; Notni, Gunther

doi:10.1016/j.optlaseng.2019.05.009

Cited by 28 publications

(9 citation statements)

References 20 publications

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“…22 For the stereo calibration of thermal cameras, the application of printed circuit boards have become established. 16,[23][24][25][26] The key advantage of a printed circuit board is that the surfaces of the printed areas and the board are made of two materials with very different emissivities. Even if the calibration board has a homogeneous temperature distribution, the features are easily recognizable with thermal cameras due to the very different emissivities (see Fig.…”

Section: Calibrationmentioning

confidence: 99%

High-speed 3D shape measurement of transparent objects by sequential thermal fringe projection and image acquisition in the long-wave infrared

Landmann¹,

Speck²,

Gao³

et al. 2023

Thermosense: Thermal Infrared Applications XLV

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Recently, we have successfully tackled the challenge of measuring the 3D shape of uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent. By projecting sequential thermal fringes in the long-wave infrared (LWIR) combined with a stereo camera setup in the midwave infrared (MWIR), we were able to three-dimensionally record object shapes within one second. However, in many applications, e.g., for 100 % quality assurance, even shorter measurement times are required. To achieve camera frame rates higher than 125 fps at room temperature, Max Planck’s law of thermal emission teaches us a change in the camera spectral range from MWIR to LWIR. If irradiation and image acquisition have to run in parallel, the camera chips must therefore be protected against the radiation projected by the CO2 laser at a wavelength of 10.6 µm. Appropriate filters have been available only recently. In this contribution, we present our high-speed LWIR 3D sensor. The work includes a characterization of our setup regarding its measurement accuracy and speed. The results are compared to the performance of previous thermal 3D sensors. We show 3D measurement results of static objects as well as of a dynamic measurement situation of a transparent object. Furthermore, we demonstrate that our setup enables us to extend the measurability of material classes towards objects with high thermal conductivities.

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Section: Calibrationmentioning

confidence: 99%

High-speed 3D shape measurement of transparent objects by sequential thermal fringe projection and image acquisition in the long-wave infrared

Landmann¹,

Speck²,

Gao³

et al. 2023

Thermosense: Thermal Infrared Applications XLV

Self Cite

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“…This technique has proved to work well, especially for applications where commercial projectors are not available (e.g., extended spectral ranges, high camera frame rates of 10 kHz or higher). We have realized irritation free (near infrared-NIR) facial measurement systems [19], high speed measurement systems to measure fast human body motions [20] or air-bag inflations [21], and a system with a thermal laser projector to measure glass and transparent plastic objects [22].…”

Section: Stereo Vision Based 3d Sensorsmentioning

confidence: 99%

BICOS—An Algorithm for Fast Real-Time Correspondence Search for Statistical Pattern Projection-Based Active Stereo Sensors

et al. 2019

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Pattern projection-based 3D measurement systems are widely used for contactless, non-destructive optical 3D shape measurements. In addition, many robot-operated automation tasks require real-time reconstruction of accurate 3D data. In previous works, we have demonstrated 3D scanning based on statistical pattern projection-aided stereo matching between two cameras. One major advantage of this technology is that the actually projected patterns do not have to be known a priori in the reconstruction software. This allows much simpler projector designs and enables high-speed projection. However, to find corresponding pixels between cameras, it is necessary to search the best match amongst all pixels within the geometrically possible image area (that is, within a range on the corresponding epipolar line). The well-established method for this search is to compare each candidate pixel by temporal normalized cross correlation of the brightness value sequences of both pixels. This is computationally expensive and interdicts fast real-time applications on inexpensive computer hardware. We show two variants of our algorithm “Binary Correspondence Search” (BICOS), which solve this task in significantly reduced calculation time. In practice, our algorithm is much faster than traditional, purely cross-correlation-based search while maintaining a similar level of accuracy.

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“…3D thermal imaging techniques described in the literature generally embrace three categories: (1) combination of 2D IR imaging and depth data 10 – 14 , (2) reconstruction based on sensors attached to the object surface 15 , and (3) reconstruction from a series of 2D IR images (thermal tomography) 16 , 17 . The first group returns the spatial shape and surface temperature distribution, while the other two address the temperature distribution within the object.…”

Section: Introductionmentioning

confidence: 99%

“…The first group returns the spatial shape and surface temperature distribution, while the other two address the temperature distribution within the object. Landmann et al 10 reported a method that combined 2D IR data and depth data in the form of a high-speed acquisition tool constructed of the light sensor and long-wave IR camera. The system returns a 3D model with temperature data on its surface.…”

Section: Introductionmentioning

confidence: 99%

Tomographic reconstruction from planar thermal imaging using convolutional neural network

Ledwoń

Sage

Juszczyk

et al. 2022

Sci Rep

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In this study, we investigate perspectives for thermal tomography based on planar infrared thermal images. Volumetric reconstruction of temperature distribution inside an object is hardly applicable in a way similar to ionizing-radiation-based modalities due to its non-penetrating character. Here, we aim at employing the autoencoder deep neural network to collect knowledge on the single-source heat transfer model. For that purpose, we prepare a series of synthetic 3D models of a cylindrical phantom with assumed thermal properties with various heat source locations, captured at different times. A set of planar thermal images taken around the model is subjected to initial backprojection reconstruction, then passed to the deep model. This paper reports the training and testing results in terms of five metrics assessing spatial similarity between volumetric models, signal-to-noise ratio, or heat source location accuracy. We also evaluate the assumptions of the synthetic model with an experiment involving thermal imaging of a real object (pork) and a single heat source. For validation, we investigate objects with multiple heat sources of a random location and temperature. Our results show the capability of a deep model to reconstruct the temperature distribution inside the object.

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High-speed 3D thermography

Cited by 28 publications

References 20 publications

High-speed 3D shape measurement of transparent objects by sequential thermal fringe projection and image acquisition in the long-wave infrared

High-speed 3D shape measurement of transparent objects by sequential thermal fringe projection and image acquisition in the long-wave infrared

BICOS—An Algorithm for Fast Real-Time Correspondence Search for Statistical Pattern Projection-Based Active Stereo Sensors

Tomographic reconstruction from planar thermal imaging using convolutional neural network

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