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Open access

Njord: a fishing trawler dataset

Authors:

Tor-Arne Schmidt Nordmo,

Aril Bernhard Ovesen,

Bjørn Aslak Juliussen,

Steven Alexander Hicks,

Vajira Thambawita,

Håvard Dagenborg Johansen,

Pål Halvorsen,

Michael Alexander Riegler,

Dag JohansenAuthors Info & Claims

MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference

Pages 197 - 202

https://doi.org/10.1145/3524273.3532886

Published: 05 August 2022 Publication History

Abstract

Fish is one of the main sources of food worldwide. The commercial fishing industry has a lot of different aspects to consider, ranging from sustainability to reporting. The complexity of the domain also attracts a lot of research from different fields like marine biology, fishery sciences, cybernetics, and computer science. In computer science, detection of fishing vessels via for example remote sensing and classification of fish from images or videos using machine learning or other analysis methods attracts growing attention. Surprisingly, little work has been done that considers what is happening on board the fishing vessels. On the deck of the boats, a lot of data and important information are generated with potential applications, such as automatic detection of accidents or automatic reporting of fish caught. This paper presents Njord, a fishing trawler dataset consisting of surveillance videos from a modern off-shore fishing trawler at sea. The main goal of this dataset is to show the potential and possibilities that analysis of such data can provide. In addition to the data, we provide a baseline analysis and discuss several possible research questions this dataset could help answer.

References

[1]

Gerard Gilbert. 2014. Slow Television: The latest Nordic trend. https://www.independent.co.uk/arts-entertainment/tv/features/slow-television-chess-trains-and-knitting-9122367.html

[2]

Glenn Jocher. 2020. ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements.

[3]

Labelbox. 2022. Labelbox. https://labelbox.com

[4]

Daoliang Li, Qi Wang, Xin Li, Meilin Niu, He Wang, and Chunhong Liu. 2022. Recent advances of machine vision technology in fish classification. ICES Journal of Marine Science (2022).

[5]

Mi-Ling Li, Yoshitaka Ota, Philip J Underwood, Gabriel Reygondeau, Katherine Seto, Vicky WY Lam, David Kroodsma, and William WL Cheung. 2021. Tracking industrial fishing activities in African waters from space. Fish and Fisheries 22, 4 (2021), 851--864.

[6]

Alihan Mermer, TÜRK Meral, and Zafer Tosunoğlu. 2022. Occupational health and safety in large-scale fishing vessels registered in Aegean ports. Ege Journal of Fisheries and Aquatic Sciences 39, 1 (2022), 18--23.

[7]

Jaeyoon Park, Jungsam Lee, Katherine Seto, Timothy Hochberg, Brian A Wong, Nathan A Miller, Kenji Takasaki, Hiroshi Kubota, Yoshioki Oozeki, Sejal Doshi, et al. 2020. Illuminating dark fishing fleets in North Korea. Science advances 6, 30 (2020), eabb1197.

[8]

Alzayat Saleh, Issam H Laradji, Dmitry A Konovalov, Michael Bradley, David Vazquez, and Marcus Sheaves. 2020. A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis. Scientific Reports 10, 1 (2020), 1--10.

[9]

Monique Simier, Jean-Marc Ecoutin, and Luis Tito de Morais. 2019. The PPEAO experimental fishing dataset: Fish from West African estuaries, lagoons and reservoirs. Biodiversity Data Journal 7 (2019).

[10]

Paolo Spagnolo, Francesco Filieri, Cosimo Distante, Pier Luigi Mazzeo, and Paolo D'Ambrosio. 2019. A new annotated dataset for boat detection and re-identification. In 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 1--7.

[11]

Kaiyu Tang, Yixin Bao, Zhijian Zhao, Liang Zhu, Yining Lin, and Yao Peng. 2018. AutoHighlight : Automatic Highlights Detection and Segmentation in Soccer Matches. In 2018 IEEE International Conference on Big Data (Big Data). 4619--4624.

[12]

Jiangping Wang, Anthony Pillay, YS Kwon, AD Wall, and CG Loughran. 2005. An analysis of fishing vessel accidents. Accident Analysis & Prevention 37, 6 (2005), 1019--1024.

[13]

Tianwen Zhang, Xiaoling Zhang, Xiao Ke, Chang Liu, Xiaowo Xu, Xu Zhan, Chen Wang, Israr Ahmad, Yue Zhou, Dece Pan, et al. 2021. HOG-ShipCLSNet: A novel deep learning network with hog feature fusion for SAR ship classification. IEEE Transactions on Geoscience and Remote Sensing 60 (2021), 1--22.

[14]

Tianwen Zhang, Xiaoling Zhang, Xiao Ke, Xu Zhan, Jun Shi, Shunjun Wei, Dece Pan, Jianwei Li, Hao Su, Yue Zhou, et al. 2020. LS-SSDD-v1. 0: A deep learning dataset dedicated to small ship detection from large-scale Sentinel-1 SAR images. Remote Sensing 12, 18 (2020), 2997.

[15]

Yue Zhang, Masato Yamamoto, Genki Suzuki, and Hiroyuki Shioya. 2022. Collaborative Forecasting and Analysis of Fish Catch in Hokkaido from Multiple Scales by Using Neural Network and ARIMA Model. IEEE Access (2022).

Cited By

Torpmann-Hagen BRiegler MHalvorsen PJohansen D(2024)A Robust Framework for Distributional Shift Detection Under Sample-BiasIEEE Access10.1109/ACCESS.2024.339329612(59598-59611)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3393296
Ovesen ANordmo TJohansen D(2023)Compliant multimedia storage and data extraction from the untrusted and privacy-sensitive edge2023 International Conference on Multimedia Computing, Networking and Applications (MCNA)10.1109/MCNA59361.2023.10185828(123-130)Online publication date: 19-Jun-2023
https://doi.org/10.1109/MCNA59361.2023.10185828

Index Terms

Njord: a fishing trawler dataset
1. Applied computing
  1. Law, social and behavioral sciences
    1. Law
2. Computing methodologies
  1. Machine learning
    1. Cross-validation
    2. Learning paradigms
      1. Supervised learning
        Supervised learning by regression

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Information & Contributors

Information

Published In

cover image ACM Conferences

MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference

June 2022

432 pages

ISBN:9781450392839

DOI:10.1145/3524273

General Chairs:
Niall Murray
Technological University of the Shannon: Midlands Midwest
,
Gwendal Simon
Synamedia
,
Mylene Farias
University of Brasilia
,
Program Chairs:
Irene Viola
Centrum Wiskunde & Informatica
,
Mario Montagud
i2CAT Foundation & University of Valencia

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 August 2022

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Research-article

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Research Council of Norway

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MMSys '22

Sponsor:

SIGMM

MMSys '22: 13th ACM Multimedia Systems Conference

June 14 - 17, 2022

Athlone, Ireland

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Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Torpmann-Hagen BRiegler MHalvorsen PJohansen D(2024)A Robust Framework for Distributional Shift Detection Under Sample-BiasIEEE Access10.1109/ACCESS.2024.339329612(59598-59611)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3393296
Ovesen ANordmo TJohansen D(2023)Compliant multimedia storage and data extraction from the untrusted and privacy-sensitive edge2023 International Conference on Multimedia Computing, Networking and Applications (MCNA)10.1109/MCNA59361.2023.10185828(123-130)Online publication date: 19-Jun-2023
https://doi.org/10.1109/MCNA59361.2023.10185828

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