  Cameras are an essential part of sensor suite in autonomous driving.
Surround-view cameras are directly exposed to external environment and are
vulnerable to get soiled. Cameras have a much higher degradation in performance
due to soiling compared to other sensors. Thus it is critical to accurately
detect soiling on the cameras, particularly for higher levels of autonomous
driving. We created a new dataset having multiple types of soiling namely
opaque and transparent. It will be released publicly as part of our WoodScape
dataset \cite{yogamani2019woodscape} to encourage further research. We
demonstrate high accuracy using a Convolutional Neural Network (CNN) based
architecture. We also show that it can be combined with the existing object
detection task in a multi-task learning framework. Finally, we make use of
Generative Adversarial Networks (GANs) to generate more images for data
augmentation and show that it works successfully similar to the style transfer.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Robotics;Machine Learning