  The lack of effective target regions makes it difficult to perform several
visual functions in low intensity light, including pedestrian recognition, and
image-to-image translation. In this situation, with the accumulation of
high-quality information by the combined use of infrared and visible images it
is possible to detect pedestrians even in low light. In this study we are going
to use advanced deep learning models like pix2pixGAN and YOLOv7 on LLVIP
dataset, containing visible-infrared image pairs for low light vision. This
dataset contains 33672 images and most of the images were captured in dark
scenes, tightly synchronized with time and location.
