SoilingNet: Soiling Detection on Automotive Surround-View Cameras

By Michal Uřičář, Pavel Krízek, Ganesh Sistu, Senthil Kumar Yogamani. 2019

58 citations recorded by the source corpus.

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 [15] 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.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “This is achieved by a combination of several perception tasks like semantic segmentation [3], [4], motion estimation [5], depth estimation [6], soiling detection [7], etc which can be efficiently unified”