TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric
Open accessAbstract
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 [1] to encourage further research.
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Existing studies mainly focused on classification or patch-based detection, providing limited spatial understanding of contamination.
“However, these studies mainly focused on classification or patch-based detection, providing limited spatial understanding of contamination.”
From AI-Based Image Quality and Lens Defect Analysis in Autonomous Driving: A Framework with U-Net-Based Soiling Detection (2025) · cites this paper as [2]
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- Authors
- Das, A., Krizek, P., Sistu, G., Burger, F., Madasamy, S., Uricar, M., Kumar, V. R., & Yogamani, S.
- Affiliations
- Valeo (Ireland) (IE)
- Valeo (Germany) (DE)
- Venue
- 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
- Publisher
- IEEE
- Published
- 2020-09-20
- Pages
- 1-6
- OpenAlex
- W3040343572
- Cited by
- 25
- References
- 33
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 7 · 2021: 4
- FWCI
- 1.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- soiling detection, autonomous driving, multi-task learning, object detection, semantic segmentation
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