Synthetic images of corals (Desmophyllum pertusum) with object detection models

SND-ID: 2022-98

Creator/Principal investigator(s)

Matthias Obst - University of Gothenburg, Department of Marine Sciences orcid

Sarah Al-Khateeb - MMT Sweden AB / Ocean Infinity

Victor Anton - wildlife.ai orcid

Jannes Germishuys - Combine AB

Description

Two object detection models using Darknet/YOLOv4 were trained on images of the coral Desmophyllum pertusum from the Kosterhavet National Park. In one of the models, the training image data was amplified using StyleGAN2 generative modeling.
The dataset contains 2266 synthetic images with labels and 409 original images of corals used for training the ML model. Included is also the YOLOv4 models and the StyleGAN2 network.

Language

English

Research principal, contributors, and funding

Research principal

University of Gothenburg

Responsible department/unit

Department of Marine Sciences

Funding 1

  • Funding agency: Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) rorId
  • Funding agency's reference number: 2021-02465_Formas
  • Project name on the application: National implementation of a platform for analysis of sub-sea images (PLAN-SUBSIM)
  • Funding information: The data set collection was funded though Swedish Biodiversity Data Infrastructure (SRC), Ocean Data Factory (Vinnova), and PLAN-SUBSIM (FORMAS)

Funding 2

  • Funding agency: Swedish Research Council rorId
  • Funding agency's reference number: 2019-00242
  • Project name on the application: Swedish Biodiversity Data Infrastructure
  • Funding information: The data set collection was funded though Swedish Biodiversity Data Infrastructure (SRC), Ocean Data Factory (Vinnova), and PLAN-SUBSIM (FORMAS)

Funding 3

  • Funding agency: Vinnova rorId
  • Funding agency's reference number: 2019-02256
  • Project name on the application: Ocean Data Factory
  • Funding information: The data set collection was funded though Swedish Biodiversity Data Infrastructure (SRC), Ocean Data Factory (Vinnova), and PLAN-SUBSIM (FORMAS)
Protection and ethical review

Data contains personal data

No

Method and time period

Time period(s) investigated

1999 – 2001

Species and taxons

desmophyllum pertusum
lophelia pertusa (formerly)

Geographic coverage

Geographic spread

Geographic location: Sweden

Geographic description: Kosterhavet National Park

Publications

Alkhateeb, Sarah, Obst, Matthias, Anton, Victor and Germishuys Jannes. (2023). A methodology to detect deepwater corals using Generative Adversarial Networks. GigaScience. [Submitted manuscript].

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Dataset
Synthetic images of corals (Desmophyllum pertusum) with object detection models

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Description

The still images were extracted from raw video data collected using a remotely operated underwater vehicle.
409 JPEG images from the raw video data are provided in 720x576 resolution. In certain images, coordinates visible in the OSD have been cropped.
The synthetic images are PNG files in 512x512 resolution.
The StyleGAN2 network is included as a serialized pickle file (*.pkl).
The object detection models are provided in the .weights format used by the Darknet/YOLOv4 package. Two files are inc

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Version 1

Citation

Matthias Obst, Sarah Al-Khateeb, Victor Anton, Jannes Germishuys. University of Gothenburg (2023). Synthetic images of corals (Desmophyllum pertusum) with object detection models. Swedish National Data Service. Version 1. https://doi.org/10.5878/hp35-4809

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Data format / data structure

Still image

Software

Creator/Principal investigator(s)

Matthias Obst - University of Gothenburg, Department of Marine Sciences orcid

Sarah Al-Khateeb - MMT Sweden AB / Ocean Infinity

Victor Anton - wildlife.ai orcid

Jannes Germishuys - Combine AB

Data collection 1

  • Mode of collection: Recording
  • Description of the mode of collection: Video recordings from 35 research cruises in the Kosterhavet National Park using a ROV.
  • Time period(s) for data collection: 1999–2004
  • Data collector: Department of Marine Sciences, University of Gothenburg

Data collection 2

  • Mode of collection: Transcription
  • Description of the mode of collection: The classification of Desmophyllum pertusum in still images from the video data has been performed as citizen science by volunteers using the classification tool on the Koster seafloor observatory website.
  • Data collector: The Koster seafloor observatory

Contact for questions about the data

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Published: 2023-04-12
Last updated: 2023-05-03