# Automated Marine Biofouling Assessment: Benchmarking Computer Vision and Multimodal LLMs on the Level of Fouling Scale

arXiv: https://arxiv.org/abs/2601.20196 · HTML: https://mlsanity.com/paper/2601.20196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Diving Deeper into Underwater Image Enhancement: A Survey](https://mlsanity.com/paper/1907.07863/) (2019)
- [LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptions](https://mlsanity.com/paper/2311.11904/) (2023)
- [Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study](https://mlsanity.com/paper/2401.02147/) (2024)
- [Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs](https://mlsanity.com/paper/2406.01943/) (2024)
- [Improving underwater semantic segmentation with underwater image quality attention and muti-scale aggregation attention](https://mlsanity.com/paper/2503.23422/) (2025)
- [Automating the assessment of biofouling in images using expert agreement as a gold standard](https://mlsanity.com/paper/2008.09289/) (2020)
- [An interpretable approach to automating the assessment of biofouling in video footage](https://mlsanity.com/paper/2503.12875/) (2025)

## Abstract (arXiv)

Marine biofouling on vessel hulls poses major ecological, economic, and biosecurity risks. Traditional survey methods rely on diver inspections, which are hazardous and limited in scalability. This work investigates automated classification of biofouling severity on the Level of Fouling (LoF) scale using both custom computer vision models and large multimodal language models (LLMs). Convolutional neural networks, transformer-based segmentation, and zero-shot LLMs were evaluated on an expert-labelled dataset from the New Zealand Ministry for Primary Industries. Computer vision models showed high accuracy at extreme LoF categories but struggled with intermediate levels due to dataset imbalance and image framing. LLMs, guided by structured prompts and retrieval, achieved competitive performance without training and provided interpretable outputs. The results demonstrate complementary strengths across approaches and suggest that hybrid methods integrating segmentation coverage with LLM reasoning offer a promising pathway toward scalable and interpretable biofouling assessment.