Several visual tasks, such as pedestrian detection and image-to-image translation, are challenging to accomplish in low light using RGB images. Heat variation of objects in thermal images can be used to overcome this. In this work, an end-to-end framework, which consists of a generative network and a detector network, is proposed to translate RGB image into Thermal ones and compare generated thermal images with real data. We have collected images from two different locations using the Parrot Anafi Thermal drone. After that, we created a two-stream network, preprocessed, augmented, the image data, and trained the generator and discriminator models from scratch. The findings demonstrate that it is feasible to translate RGB training data to thermal data using GAN. As a result, thermal data can now be produced more quickly and affordably, which is useful for security and surveillance applications.

翻译：多项视觉任务，如行人检测和图像到图像翻译，在低光照条件下使用RGB图像难以完成。利用热红外图像中物体的热变化可以克服这一难题。本文提出了一种由生成网络和检测网络组成的端到端框架，用于将RGB图像转换为热红外图像，并将生成的图像与实际数据进行比较。我们使用Parrot Anafi Thermal无人机从两个不同地点采集图像，随后构建双流网络，对图像数据进行预处理和增强，并从头训练生成器与判别器模型。结果表明，使用生成对抗网络（GAN）将RGB训练数据转换为热红外数据是可行的。由此，可更快速、低成本地生成热红外数据，这对安防监控应用具有重要意义。