Author

R. Connolly

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Ecology· 4

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Approach:deep learning· 3 Research Focus:ecology & biodiversity· 3 Assay:imaging· 3 Organism:zebrafish· 3 Research Focus:cell type annotation· 1 Approach:statistical model· 1

Frequent Co-Authors

E. Ditria· 3 shared E. Jinks· 3 shared S. Lopez-Marcano· 3 shared C. J. Brown· 2 shared M. Sievers· 2 shared A. Olds· 1 shared C. Saint Ange· 1 shared D. Fairclough· 1 shared

Activity by Year

2019-2021 · peak 2/yr

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10 top collaborators · 25 mutual co-author links

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3 shared papersEcology

E. Ditria

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4 papers

Recent·Most cited

Ecosystem services in connected catchment to coast ecosystems: monitoring to detect emerging trends

C. J. Brown, C. Saint Ange, R. Connolly, S. Hasan, S. Jackson, J. McMahon, J. Smart

The paper tests ecological indicators in a catchment-to-coast system to determine their response times to environmental change, finding they require over 10 years to detect trends.

bioRxiv · Jul 19, 2021Ecology

Improved accuracy for automated counting of a fish in baited underwater videos for stock assessment

R. Connolly, D. Fairclough, E. Jinks, E. Ditria, G. Jackson, S. Lopez-Marcano, A. Olds, K. Jinks

This paper improves automated counting of fish in BRUVS videos by optimizing confidence thresholds, Seq-NMS, and correction equations to achieve high accuracy across fish abundances.

bioRxiv · Feb 1, 2021 · PreprintEcology

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

E. Ditria, M. Sievers, R. Connolly, E. Jinks, S. Lopez-Marcano

This study trains deep learning models to detect luderick fish in coastal habitats, finding that models trained on multiple habitats perform better across different environments.

bioRxiv · May 19, 2020Ecology

Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

E. Ditria, S. Lopez-Marcano, E. Jinks, M. Sievers, C. J. Brown, R. Connolly

This study demonstrates that deep learning outperforms human experts and citizen scientists in accurately and efficiently quantifying fish abundance from underwater footage.

bioRxiv · Oct 16, 2019Ecology