# SoilingNet: Soiling Detection on Automotive Surround-View Cameras

arXiv: https://arxiv.org/abs/1905.01492 · HTML: https://mlsanity.com/paper/1905.01492/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2019.

## Built on by (most cited first, 14 of 14)

- [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021)
- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning](https://mlsanity.com/paper/2104.14042/) (2021)
- [Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions](https://mlsanity.com/paper/2103.02760/) (2021)
- [Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing](https://mlsanity.com/paper/2201.03246/) (2022)
- [Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving](https://mlsanity.com/paper/2105.07930/) (2021)
- [Robustness of Object Detection of Autonomous Vehicles in Adverse Weather Conditions](https://mlsanity.com/paper/2602.12902/) (2026)
- [Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails](https://mlsanity.com/paper/2608.14952/) (2026)
- [Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS](https://mlsanity.com/paper/2101.07831/) (2021)
- [A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applications](https://mlsanity.com/paper/2303.07100/) (2023)
- [Challenges in automatic and selective plant-clearing](https://mlsanity.com/paper/2404.13996/) (2024)
- [Soiling detection for Advanced Driver Assistance Systems](https://mlsanity.com/paper/2511.09740/) (2025)
- [De-occluding broadband metalens](https://mlsanity.com/paper/2601.19403/) (2026)

## Abstract (arXiv)

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 citeyogamani2019woodscape 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.