SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving

Abstract

The perception of the environment plays a decisive role in the safe and secure operation of autonomous vehicles. The perception of the surrounding is way similar to human vision. The human's brain perceives the environment by utilizing different sensory channels and develop a view-invariant representation model. In this context, different exteroceptive sensors like cameras, Lidar, are deployed on the autonomous vehicle to perceive the environment. These sensors have illustrated their benefit in the visible spectrum domain yet in the adverse weather conditions; for instance, they have limited operational capability at night, leading to fatal accidents. This work explores thermal object detection to model a view-invariant model representation by employing the self-supervised contrastive learning approach. 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. Later, these learned feature representations are employed for thermal object detection using a 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.