  Autonomous driving significantly benefits from data-driven deep neural
networks. However, the data in autonomous driving typically fits the
long-tailed distribution, in which the critical driving data in adverse
conditions is hard to collect. Although generative adversarial networks (GANs)
have been applied to augment data for autonomous driving, generating driving
images in adverse conditions is still challenging. In this work, we propose a
novel SUSTechGAN with dual attention modules and multi-scale generators to
generate driving images for improving object recognition of autonomous driving
in adverse conditions. We test the SUSTechGAN and the existing well-known GANs
to generate driving images in adverse conditions of rain and night and apply
the generated images to retrain object recognition networks. Specifically, we
add generated images into the training datasets to retrain the well-known
YOLOv5 and evaluate the improvement of the retrained YOLOv5 for object
recognition in adverse conditions. The experimental results show that the
generated driving images by our SUSTechGAN significantly improved the
performance of retrained YOLOv5 in rain and night conditions, which outperforms
the well-known GANs. The open-source code, video description and datasets are
available on the page 1 to facilitate image generation development in
autonomous driving under adverse conditions.
