Three-dimensional temperature distribution mapping by generative adversarial network in low light environment using thermography

Shohei Oka; Yonghoon Ji; Hiromitsu Fujii; Hitoshi Kono

doi:10.1117/12.3000051

28 July 2023 Three-dimensional temperature distribution mapping by generative adversarial network in low light environment using thermography

Shohei Oka, Yonghoon Ji, Hiromitsu Fujii, Hitoshi Kono

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Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 1274913 (2023) https://doi.org/10.1117/12.3000051
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France

Abstract

In this study, we propose a new framework to perform visual simultaneous localization and mapping (SLAM) with RGB images artificially generated from thermal images in low light environments where an optical camera cannot be applied. We applied contrastive unpaired translation (CUT) and enhanced generative adversarial network for super-resolution (ESRGAN), which are image translation methods to generate a clear realistic RGB image from a thermal image. Oriented FAST and rotated BRIEF (ORB)-SLAM was performed using the super-resolution fake RGB image to generate a 3D point cloud. Experimental results showed that our thermography-based visual SLAM could generate a 3D temperature distribution map in the low light environment.

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Shohei Oka, Yonghoon Ji, Hiromitsu Fujii, and Hitoshi Kono "Three-dimensional temperature distribution mapping by generative adversarial network in low light environment using thermography", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 1274913 (28 July 2023); https://doi.org/10.1117/12.3000051

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KEYWORDS

Thermography

RGB color model

Visualization

3D image processing

Education and training

Cameras

Temperature distribution

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