# Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving

arXiv: https://arxiv.org/abs/2105.07930 · HTML: https://mlsanity.com/paper/2105.07930/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019)
- [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021)
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://mlsanity.com/paper/2012.02124/) (2020)
- [I Can See Clearly Now : Image Restoration via De-Raining](https://mlsanity.com/paper/1901.00893/) (2019)
- [Challenges in Designing Datasets and Validation for Autonomous Driving](https://mlsanity.com/paper/1901.09270/) (2019)
- [Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision](https://mlsanity.com/paper/1803.06192/) (2018)
- [SoilingNet: Soiling Detection on Automotive Surround-View Cameras](https://mlsanity.com/paper/1905.01492/) (2019)
- [Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras](https://mlsanity.com/paper/2001.02161/) (2020)
- [A New Ensemble Learning Framework for 3D Biomedical Image Segmentation](https://mlsanity.com/paper/1812.03945/) (2018)
- [Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving](https://mlsanity.com/paper/1902.03442/) (2019)
- [FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System](https://mlsanity.com/paper/1912.11066/) (2019)

## Built on by (most cited first, 2 of 2)

- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [Soiling detection for Advanced Driver Assistance Systems](https://mlsanity.com/paper/2511.09740/) (2025)

## Abstract (arXiv)

Manual annotation of soiling on surround view cameras is a very challenging and expensive task. The unclear boundary for various soiling categories like water drops or mud particles usually results in a large variance in the annotation quality. As a result, the models trained on such poorly annotated data are far from being optimal. In this paper, we focus on handling such noisy annotations via pseudo-label driven ensemble model which allow us to quickly spot problematic annotations and in most cases also sufficiently fixing them. We train a soiling segmentation model on both noisy and refined labels and demonstrate significant improvements using the refined annotations. It also illustrates that it is possible to effectively refine lower cost coarse annotations.