Image-to-image translation for improvement of synthetic thermal infrared training data using generative adversarial networks

Hanna H. Hamrell; Jörgen M. Karlholm

doi:10.1117/12.2599214

12 September 2021 Image-to-image translation for improvement of synthetic thermal infrared training data using generative adversarial networks

Hanna H. Hamrell, Jörgen M. Karlholm

Author Affiliations +

Proceedings Volume 11870, Artificial Intelligence and Machine Learning in Defense Applications III; 118700F (2021) https://doi.org/10.1117/12.2599214
Event: SPIE Security + Defence, 2021, Online Only

Abstract

Training data is an essential ingredient within supervised learning, but time consuming, expensive and for some applications impossible to acquire. A possible solution is to use synthetic training data. However, the domain shift of synthetic data makes it challenging to obtain good results when used as training data for deep learning models. It is therefore of interest to refine synthetic data, e.g. using image-to-image translation, to improve results. The aim of this work is to compare different methods to do image-to-image translation of synthetic training data of thermal IR-images using generative adversarial networks (GANs). Translation is done both using synthetic thermal IR-images alone, as well as including pixelwise depth and/or semantic information. To evaluate, we propose a new measure based on the Frechet Inception Distance, adapted to work for thermal IR-images. We show that by adapting a GAN model to also include corresponding pixelwise depth data to each synthetic IR-image, the performance is improved compared to using only IR-images.

Conference Presentation

Citation Download Citation

Hanna H. Hamrell and Jörgen M. Karlholm "Image-to-image translation for improvement of synthetic thermal infrared training data using generative adversarial networks", Proc. SPIE 11870, Artificial Intelligence and Machine Learning in Defense Applications III, 118700F (12 September 2021); https://doi.org/10.1117/12.2599214

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