# TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric

arXiv: https://arxiv.org/abs/2007.00801 · HTML: https://mlsanity.com/paper/2007.00801/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 17 of 17)

- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving](https://mlsanity.com/paper/2203.05056/) (2022)
- [LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation](https://mlsanity.com/paper/2111.04875/) (2021)
- [Detecting Adversarial Perturbations in Multi-Task Perception](https://mlsanity.com/paper/2203.01177/) (2022)
- [Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving](https://mlsanity.com/paper/2107.07449/) (2021)
- [Revisiting Modality Imbalance In Multimodal Pedestrian Detection](https://mlsanity.com/paper/2302.12589/) (2023)
- [An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2105.12763/) (2021)
- [Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge](https://mlsanity.com/paper/2206.12912/) (2022)
- [Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View](https://mlsanity.com/paper/2402.00637/) (2024)
- [SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving](https://mlsanity.com/paper/2206.02876/) (2022)
- [Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS](https://mlsanity.com/paper/2101.07831/) (2021)
- [UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal](https://mlsanity.com/paper/2203.15441/) (2022)
- [A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applications](https://mlsanity.com/paper/2303.07100/) (2023)
- [WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge](https://mlsanity.com/paper/2401.00910/) (2023)
- [Neural Rendering based Urban Scene Reconstruction for Autonomous Driving](https://mlsanity.com/paper/2402.06826/) (2024)
- [BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation](https://mlsanity.com/paper/2503.03280/) (2025)
- [Soiling detection for Advanced Driver Assistance Systems](https://mlsanity.com/paper/2511.09740/) (2025)

## Abstract (arXiv)

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.