Cross-domain infrared target recognition based on simulation

Ning Zhang; Weihua Wang; Gao Chen

doi:10.1117/12.2600134

1 June 2021 Cross-domain infrared target recognition based on simulation

Ning Zhang, Weihua Wang, Gao Chen

Proceedings Volume 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021); 118480N (2021) https://doi.org/10.1117/12.2600134
Event: International Conference on Signal Image Processing and Communication (ICSIPC 2021), 2021, Chengdu, China

Abstract

In recent years, automatic target recognition (ATR) based on deep learning has achieved great success in RGB field, which has huge data support. However, due to the confidentiality of military targets, weather constraints, and high shooting costs, it is difficult to obtain a large number of real IR images which leads to the performance degradation of deep learning algorithms in IR field. This paper discusses the method of using simulation IR images as training set to get rid of dependence on the real image. However, there are still great differences between the original simulated image and the real image, which leads to many defects when using the original simulated image for training. Therefore, in this paper, we use cycleGAN to convert the original simulation image into the intermediate image closer to the real image based on generative adversarial networks (GAN). Finally, the effectiveness of this method is proved by experiments.

Citation Download Citation

Ning Zhang, Weihua Wang, and Gao Chen "Cross-domain infrared target recognition based on simulation", Proc. SPIE 11848, International Conference on Signal Image Processing and Communication (ICSIPC 2021), 118480N (1 June 2021); https://doi.org/10.1117/12.2600134

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