  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.
