RainSD: Rain Style Diversification Module for Image Synthesis
  Enhancement using Feature-Level Style Distribution
Hyeonjae Jeon Junghyun Seo Taesoo Kim Sungho Son Jungki Lee Gyeungho Choi Yongseob Lim
Autonomous driving technology nowadays targets to level 4 or beyond, but the
researchers are faced with some limitations for developing reliable driving
algorithms in diverse challenges. To promote the autonomous vehicles to spread
widely, it is important to address safety issues on this technology. Among
various safety concerns, the sensor blockage problem by severe weather
conditions can be one of the most frequent threats for multi-task learning
based perception algorithms during autonomous driving. To handle this problem,
the importance of the generation of proper datasets is becoming more
significant. In this paper, a synthetic road dataset with sensor blockage
generated from real road dataset BDD100K is suggested in the format of BDD100K
annotation. Rain streaks for each frame were made by an experimentally
established equation and translated utilizing the image-to-image translation
network based on style transfer. Using this dataset, the degradation of the
diverse multi-task networks for autonomous driving, such as lane detection,
driving area segmentation, and traffic object detection, has been thoroughly
evaluated and analyzed. The tendency of the performance degradation of deep
neural network-based perception systems for autonomous vehicle has been
analyzed in depth. Finally, we discuss the limitation and the future directions
of the deep neural network-based perception algorithms and autonomous driving
dataset generation based on image-to-image translation.