TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric
| Autor: | Arindam Das, Senthil Yogamani, Varun Ravi Kumar, Sankaralingam Madasamy, Ganesh Sistu, Fabian Burger, Pavel Krizek, Michal Uricar |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Automotive industry Computer Science - Computer Vision and Pattern Recognition Snow Object detection GeneralLiterature_MISCELLANEOUS Task (project management) Computer Science - Robotics visual_art Polygon Metric (mathematics) visual_art.visual_art_medium Computer vision Tile Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | ITSC |
| Popis: | Automotive cameras, particularly surround-view cameras, tend to get soiled by mud, water, snow, etc. For higher levels of autonomous driving, it is necessary to have a soiling detection algorithm which will trigger an automatic cleaning system. Localized detection of soiling in an image is necessary to control the cleaning system. It is also necessary to enable partial functionality in unsoiled areas while reducing confidence in soiled areas. Although this can be solved using a semantic segmentation task, we explore a more efficient solution targeting deployment in low power embedded system. We propose a novel method to regress the area of each soiling type within a tile directly. We refer to this as coverage. The proposed approach is better than learning the dominant class in a tile as multiple soiling types occur within a tile commonly. It also has the advantage of dealing with coarse polygon annotation, which will cause the segmentation task. The proposed soiling coverage decoder is an order of magnitude faster than an equivalent segmentation decoder. We also integrated it into an object detection and semantic segmentation multi-task model using an asynchronous back-propagation algorithm. A portion of the dataset used will be released publicly as part of our WoodScape dataset to encourage further research. Accepted for Oral Presentation at IEEE Intelligent Transportation Systems Conference (ITSC) 2020 |
| Databáze: | OpenAIRE |
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