# An interpretable approach to automating the assessment of biofouling in video footage

arXiv: https://arxiv.org/abs/2503.12875 · HTML: https://mlsanity.com/paper/2503.12875/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [On Feature Learning in the Presence of Spurious Correlations](https://mlsanity.com/paper/2210.11369/) (2022)
- [Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)](https://mlsanity.com/paper/2402.10376/) (2024)
- [Do Image Classifiers Generalize Across Time?](https://mlsanity.com/paper/1906.02168/) (2019)
- [Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation](https://mlsanity.com/paper/2212.04629/) (2022)
- [Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet](https://mlsanity.com/paper/2212.04871/) (2022)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [Automating the assessment of biofouling in images using expert agreement as a gold standard](https://mlsanity.com/paper/2008.09289/) (2020)
- [A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models](https://mlsanity.com/paper/2311.17093/) (2023)
- [ComFe: An Interpretable Head for Vision Transformers](https://mlsanity.com/paper/2403.04125/) (2024)
- [Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure](https://mlsanity.com/paper/2211.11607/) (2022)

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

- [Automated Marine Biofouling Assessment: Benchmarking Computer Vision and Multimodal LLMs on the Level of Fouling Scale](https://mlsanity.com/paper/2601.20196/) (2026)

## Abstract (arXiv)

Biofoulingunicodex2013communities of organisms that grow on hard surfaces immersed in waterunicodex2013provides a pathway for the spread of invasive marine species and diseases. To address this risk, international vessels are increasingly being obligated to provide evidence of their biofouling management practices. Verification that these activities are effective requires underwater inspections, using divers or underwater remotely operated vehicles (ROVs), and the collection and analysis of large amounts of imagery and footage. Automated assessment using computer vision techniques can significantly streamline this process, and this work shows how this challenge can be addressed efficiently and effectively using the interpretable Component Features (ComFe) approach with a DINOv2 Vision Transformer (ViT) foundation model. ComFe is able to obtain improved performance in comparison to previous non-interpretable Convolutional Neural Network (CNN) methods, with significantly fewer weights and greater transparencyunicodex2013through identifying which regions of the image contribute to the classification, and which images in the training data lead to that conclusion. All code, data and model weights are publicly released.