To evaluate the driving behavior of unmanned vehicles, the testing of driving algorithms using urban road images is necessary. In this article, we propose a framework using generative adversarial networks (GANs) with structural information for image style transfer: StructureGAN and GradientGAN. Different types of urban image transfers are generated using the proposed framework, such as day to night, sunny to foggy, and summer to winter transfers. The proposed method can well maintain the integrity of foreground objects and the image structural information. Artifacts such as image distortion and foreground disappearance are eliminated. The experiments with the baseline methods indicate the effectiveness of the proposed framework, which can produce transferred images with high quality.

中文翻译：

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使用具有结构细节的生成对抗网络，对城市道路图像进行样式转换

为了评估无人驾驶车辆的驾驶行为，必须使用城市道路图像对驾驶算法进行测试。在本文中，我们提出了一个使用生成对抗网络（GAN）和结构信息进行图像样式转换的框架：StructureGAN和GradientGAN。使用建议的框架可以生成不同类型的城市图像传输，例如白天到黑夜，晴天到有雾以及夏天到冬天的传输。所提出的方法可以很好地保持前景物体和图像结构信息的完整性。消除了诸如图像失真和前景消失之类的伪影。基线方法的实验表明了所提出框架的有效性，该框架可以产生高质量的转移图像。