Surveying the deep: A review of computer vision in the benthos

Trotter, Cameron ORCID: https://orcid.org/0009-0003-6738-0968; Griffiths, Huw J. ORCID: https://orcid.org/0000-0003-1764-223X; Whittle, Rowan J. ORCID: https://orcid.org/0000-0001-6953-5829. 2025 Surveying the deep: A review of computer vision in the benthos. Ecological Informatics, 102989. 10.1016/j.ecoinf.2024.102989 (In Press)

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Abstract/Summary

The analysis of image data for benthic biodiversity monitoring is now commonplace within the domain of marine ecology. Whilst advances in imaging technologies have allowed for the collection of vast quantities of data, the curation of this has traditionally been performed manually, resulting in a bottleneck whereby data is collected faster than it can be processed. Recent years have seen marine ecologists turn to the domain of computer vision to help automate this curation process. However, as the knowledge required to build such systems spans both domains, there is a high barrier to entry. To help reduce this barrier, this paper aims to provide an introduction to computer vision-based benthic biodiversity monitoring via a comprehensive literature review. To aid ecologists, key computer vision concepts are described and example use-cases highlighted. The major challenges inherent to benthic imagery for computer vision systems are explored, alongside a discussion of how current systems attempt to mitigate against these. To aid computer scientists wishing to enter the domain, an exploration of currently available open-source benthic datasets is also provided. Recommendations for future research are explored, including a move towards human-centric techniques, committing to ablation studies, reaching community agreement on open-source benchmarking datasets, and an increased use of innovative methods to allow for improved answering of key benthic ecology questions.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.ecoinf.2024.102989
ISSN:	15749541
Additional Keywords:	Benthos, Biodiversity monitoring, Computer vision, Deep learning, Instance segmentation, Image classification, Machine learning, Object detection, Semantic segmentation
Date made live:	23 Jan 2025 09:46 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535878

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