  The sensibility and sensitivity of the environment play a decisive role in
the safe and secure operation of autonomous vehicles. This perception of the
surrounding is way similar to human visual representation. The human's brain
perceives the environment by utilizing different sensory channels and develop a
view-invariant representation model. Keeping in this context, different
exteroceptive sensors are deployed on the autonomous vehicle for perceiving the
environment. The most common exteroceptive sensors are camera, Lidar and radar
for autonomous vehicle's perception. Despite being these sensors have
illustrated their benefit in the visible spectrum domain yet in the adverse
weather conditions, for instance, at night, they have limited operation
capability, which may lead to fatal accidents. In this work, we explore thermal
object detection to model a view-invariant model representation by employing
the self-supervised contrastive learning approach. For this purpose, we have
proposed a deep neural network Self Supervised Thermal Network (SSTN) for
learning the feature embedding to maximize the information between visible and
infrared spectrum domain by contrastive learning, and later employing these
learned feature representation for the thermal object detection using
multi-scale encoder-decoder transformer network. The proposed method is
extensively evaluated on the two publicly available datasets: the FLIR-ADAS
dataset and the KAIST Multi-Spectral dataset. The experimental results
illustrate the efficacy of the proposed method.
