Let’s Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
| Autor: | Fabian Burger, Hazem Rashed, Antonin Vobecky, Pavel Krizek, Senthil Yogamani, Michal Uricar, Ganesh Sistu, Varun Ravi Kumar |
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| Rok vydání: | 2021 |
| Předmět: |
Adverse weather
Visual perception Computer science business.industry 02 engineering and technology Image segmentation 010501 environmental sciences 01 natural sciences GeneralLiterature_MISCELLANEOUS Camera lens Manual annotation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | WACV |
| DOI: | 10.1109/wacv48630.2021.00081 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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