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Several visual tasks, such as pedestrian detection and image-to-imagetranslation, are challenging to accomplish in low light using RGB images. Heatvariation of objects in thermal images can be used to overcome this. In thiswork, an end-to-end framework, which consists of a generative network and adetector network, is proposed to translate RGB image into Thermal ones andcompare generated thermal images with real data. We have collected images fromtwo different locations using the Parrot Anafi Thermal drone. After that, wecreated a two-stream network, preprocessed, augmented, the image data, andtrained the generator and discriminator models from scratch. The findingsdemonstrate that it is feasible to translate RGB training data to thermal datausing GAN. As a result, thermal data can now be produced more quickly andaffordably, which is useful for security and surveillance applications.

Visible to Thermal image Translation for improving visual task in low light conditions