ArticleScientific reports2018
Tracking Fish Abundance by Underwater Image Recognition.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Recent Trends, Challenges, Privacy and Security Issues of Underwater Internet of Things.Sensors (Basel, Switzerland) · 2021Pooled it
- Welfare implications of injuries and deformities in wild fish.Animal welfare (South Mimms, England) · 2026Review
- Automated underwater plectropomus leopardus phenotype measurement through cylinder.Scientific reports · 2025Article
- Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels.Sensors (Basel, Switzerland) · 2024Article
- DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention.Animals : an open access journal from MDPI · 2024Article
- A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA.Scientific data · 2023Article
- An automated image-based workflow for detecting megabenthic fauna in optical images with examples from the Clarion-Clipperton Zone.Scientific reports · 2023Article
- Application of a deep learning image classifier for identification of Amazonian fishes.Ecology and evolution · 2023Article
- Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive Robotic Fish.Sensors (Basel, Switzerland) · 2023Article
- Image dataset for benchmarking automated fish detection and classification algorithms.Scientific data · 2023Article
- Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean.Scientific reports · 2022Article
- A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.PloS one · 2022Article
- Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach.Journal of healthcare engineering · 2022Review
- Automatic detection of fish and tracking of movement for ecology.Ecology and evolution · 2021Article
- Identifying geographically differentiated features of Ethopian Nile tilapia (Oreochromis niloticus) morphology with machine learning.PloS one · 2021Article
- Article
- An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms ofSensors (Basel, Switzerland) · 2020Article
- Deep-learning-based information mining from ocean remote-sensing imagery.National science review · 2020Article
- Towards Naples Ecological REsearch for Augmented Observatories (NEREA): The NEREA-Fix Module, a Stand-Alone Platform for Long-Term Deep-Sea Ecosystem Monitoring.Sensors (Basel, Switzerland) · 2020Article
- The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Marine cabled video-observatories allow the non-destructive sampling of species at frequencies and durations that have never been attained before. Nevertheless, the lack of appropriate methods to automatically process video imagery limits this technology for the purposes of ecosystem monitoring. Automation is a prerequisite to deal with the huge quantities of video footage captured by cameras, which can then transform these devices into true autonomous sensors. In this study, we have developed a novel methodology that is based on genetic programming for content-based image analysis. Our aim was to capture the temporal dynamics of fish abundance. We processed more than 20,000 images that were acquired in a challenging real-world coastal scenario at the OBSEA-EMSO testing-site. The images were collected at 30-min. frequency, continuously for two years, over day and night. The highly variable environmental conditions allowed us to test the effectiveness of our approach under changing light radiation, water turbidity, background confusion, and bio-fouling growth on the camera housing. The automated recognition results were highly correlated with the manual counts and they were highly reliable when used to track fish variations at different hourly, daily, and monthly time scales. In addition, our methodology could be easily transferred to other cabled video-observatories.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.