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：在低光照条件下，诸如行人检测和图像到图像转换等多项视觉任务难以通过RGB图像完成。物体在热红外图像中的热变化特性可被用于克服这一难题。本文提出了一种包含生成网络与检测网络的端到端框架，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。我们利用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与增强，并从头训练生成器与判别器模型。实验结果表明，通过生成对抗网络（GAN）将RGB训练数据转换为热红外数据是可行的，从而能够更快速、更低成本地生成热红外数据，这对安防监控应用具有重要价值。