Research graph
References from Automated estimation of gadoid and flatfish abundance in stereo-BRUV footage using YOLO object detection. Local targets link to admitted publications; unresolved targets remain external evidence.
Recent pace of change in human impact on the world’s ocean
10.1038/s41598-019-47201-9 · 2019 · External reference
Satellite mapping reveals extensive industrial activity at sea
10.1038/s41586-023-06825-8 · 2024 · External reference
What is the economic value of coastal and marine ecosystem services? A systematic literature review
10.1016/j.marpol.2024.106033 · 2024 · External reference
Methods for estimating the occurrence and abundance of marine fishes
10.1016/b978-0-323-99036-3.00002-7 · 2025 · External reference
A review and synthesis of the benefits, drawbacks, and considerations of using traps to survey fish and decapods
10.1093/icesjms/fsad206 · 2023 · External reference
Identifying fit-for purpose methods for monitoring fish communities
10.3389/fmars.2023.1322367 · 2024 · External reference
A combined acoustic and trawl survey for efficiently estimating fish abundance
10.1016/0165-7836(95)00404-1 · 1996 · External reference
Comparison of the relative efficiencies of stereo-BRUVs and traps for sampling tropical continental shelf demersal fishes
10.1016/j.fishres.2012.01.026 · 2012 · External reference
Comparing five methods for quantifying abundance and diversity of fish assemblages in seagrass habitat
10.1016/j.ecolind.2021.107415 · 2021 · External reference
What is Big BRUVver up to? Methods and uses of baited underwater video
10.1007/s11160-016-9450-1 · 2017 · External reference
Assessing the impact of introduced infrastructure at sea with cameras: A case study for spatial scale, time and statistical power
10.1016/j.marenvres.2019.04.007 · 2019 · External reference
A comparison of stereo-BRUV, diver operated and remote stereo-video transects for assessing reef fish assemblages
10.1016/j.jembe.2019.151273 · 2020 · External reference
Camera technology for monitoring marine biodiversity and human impact
10.1002/fee.1322 · 2016 · External reference
Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective
10.3389/fmars.2022.918104 · 2022 · External reference
Object Detection With Deep Learning: A Review
10.1109/tnnls.2018.2876865 · 2019 · External reference
Detection Models: A Review and its Applications
10.1007/s11042-024-18872-y · 2024 · External reference
Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
10.3390/s20030726 · 2020 · External reference
A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis
10.1371/journal.pone.0263377 · 2022 · External reference
Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning
10.3389/fmars.2022.944582 · 2022 · External reference
Toward an artificial intelligence-assisted counting of sharks on baited video
10.1016/j.ecoinf.2024.102499 · 2024 · External reference
10.1016/j.ecoinf.2026.103713
10.1016/j.ecoinf.2026.103713 · 2026 · External reference
Improved Accuracy for Automated Counting of a Fish in Baited Underwater Videos for Stock Assessment
10.3389/fmars.2021.658135 · 2021 · External reference
Automatic detection, identification and counting of deep-water snappers on underwater baited video using deep learning
10.3389/fmars.2025.1476616 · 2025 · External reference
(CFD) v. 1.00
2025 · External reference
What are we missing? Advantages of more than one viewpoint to estimate fish assemblages using baited video
10.1098/rsos.171993 · 2018 · External reference
Site and species dependent effects of offshore wind farms on fish populations
10.1016/j.marenvres.2025.106977 · 2025 · External reference
The role of acoustic telemetry to assess the effects of offshore wind infrastructure on fish behaviour, populations and predation
10.1016/j.rser.2024.115306 · 2025 · External reference
Unresolved reference
External reference
Unresolved reference
2023 · External reference
Fine-scale proximity to offshore wind turbine foundations increases biomass of demersal fish species
10.3389/fmars.2026.1765872 · 2026 · External reference
3D photogrammetry and deep-learning deliver accurate estimates of epibenthic biomass
10.1111/2041-210x.14313 · 2024 · External reference
3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf
10.3390/rs12152466 · 2020 · External reference
Unresolved reference
2005 · External reference
The need to report effect size estimates revisited. An overview of some recommended measures of effect size
2014 · External reference
Automated detection and classification of southern African Roman seabream using mask R-CNN
10.1016/j.ecoinf.2022.101593 · 2022 · External reference
DP-FishNet: Dual-path Pyramid Vision Transformer-based underwater fish detection network
10.1016/j.eswa.2023.122018 · 2024 · External reference
YOLOv8-TF: Transformer-Enhanced YOLOv8 for Underwater Fish Species Recognition with Class Imbalance Handling
10.3390/s25061846 · 2025 · External reference
Performance of Baited Underwater Video: Does It Underestimate Abundance at High Population Densities?
10.1371/journal.pone.0127559 · 2015 · External reference
Enhancing Rare Object Detection on Roadways Through Conditional Diffusion Models for Data Augmentation
10.1109/tits.2024.3438263 · 2024 · External reference
A multi-method characterization of Elasmobranch & Cheloniidae communities of the north-eastern Red Sea and Gulf of Aqaba
10.1371/journal.pone.0275511 · 2022 · External reference
A data-centric framework for combating domain shift in underwater object detection with image enhancement
10.1007/s10489-024-06224-0 · 2025 · External reference
Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats
10.1007/s10661-020-08653-z · 2020 · External reference
Unresolved reference
2025 · External reference
Variability in behaviour of four fish species attracted to baited underwater cameras in the North Sea
10.1007/s10750-011-0672-x · 2011 · External reference
Generalised deep learning model for semi-automated length measurement of fish in stereo-BRUVS
10.3389/fmars.2023.1171625 · 2023 · External reference
10.3390/ani12151976
10.3390/ani12151976 · 1976 · External reference
Cinderella science
10.1038/450789a · 2007 · External reference
An Outlook for Deep Learning in Ecosystem Science
10.1007/s10021-022-00789-y · 2022 · External reference
Capturing dynamics in underwater videos for fish detection
10.1016/j.ecoinf.2025.103013 · 2025 · External reference
