Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this work the initial steps towards a system capable of parametrising fish schools in underwater images are presented. For this purpose a deep convolutional neural network called Optical Fish Detection Network (OFDNet) is introduced. This is based on state-of-the-art deep learning object detection architectures and carries out the task of fish detection, localization and species classification using visual data obtained by underwater cameras. This work is focused towards applications in the poorly conditioned North and Baltic Sea and is initially developed for the purpose of recognizing herring and mackerel. Based on experiments on a dataset obtained at sea, OFDNet is shown to successfully detect 66.7% of the fish included and furthermore classify 89.7% of these correctly.
Original languageEnglish
Title of host publicationProceedings of 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)
Number of pages6
PublisherIEEE
Publication date2019
Pages1-6
ISBN (Electronic)978-1-7281-0253-5
DOIs
Publication statusPublished - 2019
Event2018 IEEE OES Autonomous Underwater Vehicle Symposium - Rectory Building, University of Porto, Porto, Portugal
Duration: 6 Nov 20199 Nov 2019
Conference number: 13
https://auv2018.lsts.pt/

Workshop

Workshop2018 IEEE OES Autonomous Underwater Vehicle Symposium
Number13
LocationRectory Building, University of Porto
CountryPortugal
CityPorto
Period06/11/201909/11/2019
Internet address
Series2018 Ieee/oes Autonomous Underwater Vehicle Workshop (auv)
ISSN2377-6536

Keywords

  • Artificial intelligence
  • Deep learning
  • Convolutional neural networks
  • Object detection
  • Fish detection

Cite this

Christensen, J. H., Mogensen, L. V., Galeazzi, R., & Andersen, J. C. (2019). Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks. In Proceedings of 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV) (pp. 1-6). IEEE. 2018 Ieee/oes Autonomous Underwater Vehicle Workshop (auv) https://doi.org/10.1109/AUV.2018.8729798
Christensen, Jesper Haahr ; Mogensen, Lars Valdemar ; Galeazzi, Roberto ; Andersen, Jens Christian. / Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks. Proceedings of 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV). IEEE, 2019. pp. 1-6 (2018 Ieee/oes Autonomous Underwater Vehicle Workshop (auv)).
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title = "Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks",
abstract = "In this work the initial steps towards a system capable of parametrising fish schools in underwater images are presented. For this purpose a deep convolutional neural network called Optical Fish Detection Network (OFDNet) is introduced. This is based on state-of-the-art deep learning object detection architectures and carries out the task of fish detection, localization and species classification using visual data obtained by underwater cameras. This work is focused towards applications in the poorly conditioned North and Baltic Sea and is initially developed for the purpose of recognizing herring and mackerel. Based on experiments on a dataset obtained at sea, OFDNet is shown to successfully detect 66.7{\%} of the fish included and furthermore classify 89.7{\%} of these correctly.",
keywords = "Artificial intelligence, Deep learning, Convolutional neural networks, Object detection, Fish detection",
author = "Christensen, {Jesper Haahr} and Mogensen, {Lars Valdemar} and Roberto Galeazzi and Andersen, {Jens Christian}",
year = "2019",
doi = "10.1109/AUV.2018.8729798",
language = "English",
pages = "1--6",
booktitle = "Proceedings of 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV)",
publisher = "IEEE",
address = "United States",

}

Christensen, JH, Mogensen, LV, Galeazzi, R & Andersen, JC 2019, Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks. in Proceedings of 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV). IEEE, 2018 Ieee/oes Autonomous Underwater Vehicle Workshop (auv), pp. 1-6, 2018 IEEE OES Autonomous Underwater Vehicle Symposium, Porto, Portugal, 06/11/2019. https://doi.org/10.1109/AUV.2018.8729798

Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks. / Christensen, Jesper Haahr; Mogensen, Lars Valdemar; Galeazzi, Roberto; Andersen, Jens Christian.

Proceedings of 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV). IEEE, 2019. p. 1-6 (2018 Ieee/oes Autonomous Underwater Vehicle Workshop (auv)).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks

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PY - 2019

Y1 - 2019

N2 - In this work the initial steps towards a system capable of parametrising fish schools in underwater images are presented. For this purpose a deep convolutional neural network called Optical Fish Detection Network (OFDNet) is introduced. This is based on state-of-the-art deep learning object detection architectures and carries out the task of fish detection, localization and species classification using visual data obtained by underwater cameras. This work is focused towards applications in the poorly conditioned North and Baltic Sea and is initially developed for the purpose of recognizing herring and mackerel. Based on experiments on a dataset obtained at sea, OFDNet is shown to successfully detect 66.7% of the fish included and furthermore classify 89.7% of these correctly.

AB - In this work the initial steps towards a system capable of parametrising fish schools in underwater images are presented. For this purpose a deep convolutional neural network called Optical Fish Detection Network (OFDNet) is introduced. This is based on state-of-the-art deep learning object detection architectures and carries out the task of fish detection, localization and species classification using visual data obtained by underwater cameras. This work is focused towards applications in the poorly conditioned North and Baltic Sea and is initially developed for the purpose of recognizing herring and mackerel. Based on experiments on a dataset obtained at sea, OFDNet is shown to successfully detect 66.7% of the fish included and furthermore classify 89.7% of these correctly.

KW - Artificial intelligence

KW - Deep learning

KW - Convolutional neural networks

KW - Object detection

KW - Fish detection

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Christensen JH, Mogensen LV, Galeazzi R, Andersen JC. Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks. In Proceedings of 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV). IEEE. 2019. p. 1-6. (2018 Ieee/oes Autonomous Underwater Vehicle Workshop (auv)). https://doi.org/10.1109/AUV.2018.8729798