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Wide-angle fisheye cameras are commonly used in automated driving for parkingand low-speed navigation tasks. Four of such cameras form a surround-viewsystem that provides a complete and detailed view of the vehicle. These camerasare directly exposed to harsh environmental settings and can get soiled veryeasily by mud, dust, water, frost. Soiling on the camera lens can severelydegrade the visual perception algorithms, and a camera cleaning systemtriggered 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 thedifficulty in collecting a diverse dataset as it is a relatively rare event. Wepropose a novel GAN based algorithm for generating unseen patterns of soiledimages. Additionally, the proposed method automatically provides thecorresponding soiling masks eliminating the manual annotation cost.Augmentation of the generated soiled images for training improves the accuracyof soiling detection tasks significantly by 18% demonstrating its usefulness.The manually annotated soiling dataset and the generated augmentation datasetwill be made public. We demonstrate the generalization of our fisheye trainedGAN model on the Cityscapes dataset. We provide an empirical evaluation of thedegradation of the semantic segmentation algorithm with the soiled data.

Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving