# River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index

arXiv: https://arxiv.org/abs/2207.06388 · HTML: https://mlsanity.com/paper/2207.06388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [A survey on Semi-, Self- and Unsupervised Learning for Image Classification](https://mlsanity.com/paper/2002.08721/) (2020)
- [TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris](https://mlsanity.com/paper/2007.08097/) (2020)
- [The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation](https://mlsanity.com/paper/2108.06800/) (2021)
- [Background Mixup Data Augmentation for Hand and Object-in-Contact Detection](https://mlsanity.com/paper/2202.13941/) (2022)

## Built on by (most cited first, 1 of 1)

- [Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance](https://mlsanity.com/paper/2307.12676/) (2023)

## Abstract (arXiv)

Urban rivers provide a water environment that influences residential living. River surface monitoring has become crucial for making decisions about where to prioritize cleaning and when to automatically start the cleaning treatment. We focus on the organic mud, or "scum", that accumulates on the river's surface and contributes to the river's odor and has external economic effects on the landscape. Because of its feature of a sparsely distributed and unstable pattern of organic shape, automating the monitoring process has proved difficult. We propose a patch-wise classification pipeline to detect scum features on the river surface using mixture image augmentation to increase the diversity between the scum floating on the river and the entangled background on the river surface reflected by nearby structures like buildings, bridges, poles, and barriers. Furthermore, we propose a scum-index cover on rivers to help monitor worse grade online, collect floating scum, and decide on chemical treatment policies. Finally, we demonstrate the application of our method on a time series dataset with frames every ten minutes recording river scum events over several days. We discuss the significance of our pipeline and its experimental findings.