SoilingNet: Soiling Detection on Automotive Surround-View Cameras
| Autor: | Senthil Yogamani, Pavel Krizek, Ganesh Sistu, Michal Uricar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Automotive industry Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network Machine Learning (cs.LG) Task (project management) Computer Science - Robotics Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Computer vision 0105 earth and related environmental sciences business.industry Object detection Artificial Intelligence (cs.AI) 020201 artificial intelligence & image processing Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | ITSC |
| Popis: | 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. Accepted for Oral Presentation at IEEE Intelligent Transportation Systems Conference (ITSC) 2019 |
| Databáze: | OpenAIRE |
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