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Cross-domain Infrared Target Recognition based on Simulation

机译：基于模拟的跨域红外目标识别

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摘要

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.
机译：近年来，基于深度学习的自动目标识别（ATR）在RGB领域取得了巨大的成功，具有巨大的数据支持。 然而，由于军事目标，天气限制和高拍摄成本的机密性，难以获得大量真正的IR图像，这导致IR场中深度学习算法的性能下降。 本文讨论了使用仿真红外图像作为训练集的方法，以摆脱真实图像的依赖。 然而，原始模拟图像和真实图像之间仍存在很大的差异，这在使用原始模拟图像以进行训练时导致许多缺陷。 因此，在本文中，我们使用Cycleangan将原始模拟图像转换为基于生成的对冲网络（GaN）更靠近真实图像的中间图像。 最后，通过实验证明了这种方法的有效性。

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