  Wide-angle fisheye cameras are commonly used in automated driving for parking
and low-speed navigation tasks. Four of such cameras form a surround-view
system that provides a complete and detailed view of the vehicle. These cameras
are directly exposed to harsh environmental settings and can get soiled very
easily by mud, dust, water, frost. Soiling on the camera lens can severely
degrade the visual perception algorithms, and a camera cleaning system
triggered by a soiling detection algorithm is increasingly being deployed.
While adverse weather conditions, such as rain, are getting attention recently,
there is only limited work on general soiling. The main reason is the
difficulty in collecting a diverse dataset as it is a relatively rare event. We
propose a novel GAN based algorithm for generating unseen patterns of soiled
images. Additionally, the proposed method automatically provides the
corresponding soiling masks eliminating the manual annotation cost.
Augmentation of the generated soiled images for training improves the accuracy
of soiling detection tasks significantly by 18% demonstrating its usefulness.
The manually annotated soiling dataset and the generated augmentation dataset
will be made public. We demonstrate the generalization of our fisheye trained
GAN model on the Cityscapes dataset. We provide an empirical evaluation of the
degradation of the semantic segmentation algorithm with the soiled data.
