Synthetic images of corals (Desmophyllum pertusum) with object detection models
SND-ID: 2022-98-1. Version: 1. DOI: https://doi.org/10.5878/hp35-4809
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Citation
Creator/Principal investigator(s)
Matthias Obst
- University of Gothenburg, Department of Marine Sciences
Sarah Al-Khateeb - MMT Sweden AB / Ocean Infinity
Victor Anton
- wildlife.ai
Jannes Germishuys - Combine AB
Research principal
University of Gothenburg
- Department of Marine Sciences
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.
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 included (trained on original images only, trained on original + synthetic images).
The machine learning software packages used is currently (2022) available on Github:
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.
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 included (trained on original images only, trained on original + synthetic images).
The machine learning software packages used is currently (2022) available on Github:
StyleGAN2: https://github.com/NVlabs/stylegan2
YOLOv4: https://github.com/AlexeyAB/darknet Show less..
Data contains personal data
No
Language
Time period(s) investigated
1999 – 2001
Data format / data structure
Species and taxons
Responsible department/unit
Department of Marine Sciences
Funding 1
-
Funding agency:
Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS)
- 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
- 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
- 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)
Research area
Zoology (Standard för svensk indelning av forskningsämnen 2011)
Ecology (Standard för svensk indelning av forskningsämnen 2011)
Imagery / base maps / earth cover (INSPIRE topic categories)
Biota (INSPIRE topic categories)
Oceans (INSPIRE topic categories)
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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