  Underexposure regions are vital to construct a complete perception of the
surroundings for safe autonomous driving. The availability of thermal cameras
has provided an essential alternate to explore regions where other optical
sensors lack in capturing interpretable signals. A thermal camera captures an
image using the heat difference emitted by objects in the infrared spectrum,
and object detection in thermal images becomes effective for autonomous driving
in challenging conditions. Although object detection in the visible spectrum
domain imaging has matured, thermal object detection lacks effectiveness. A
significant challenge is scarcity of labeled data for the thermal domain which
is desiderata for SOTA artificial intelligence techniques. This work proposes a
domain adaptation framework which employs a style transfer technique for
transfer learning from visible spectrum images to thermal images. The framework
uses a generative adversarial network (GAN) to transfer the low-level features
from the visible spectrum domain to the thermal domain through style
consistency. The efficacy of the proposed method of object detection in thermal
images is evident from the improved results when used styled images from
publicly available thermal image datasets (FLIR ADAS and KAIST Multi-Spectral).
