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COGMAR - Ubiquitous cognitive computer vision for marine services

COGMAR - Ubiquitous cognitive computer vision for marine services

 

Project news

The project had two presentations at the Northern Light Deep Learning Workshop in Tromsø, January 2019. 


New paper by project partner Institute of Marine Research:

Fish species identification using a convolutional neural network trained on synthetic data,
Vaneeda Allken, Nils Olav Handegard, Shale Rosen, Tiffanie Schreyeck, Thomas Mahiout and Ketil Malde,
ICES Journal of Marine Science, 2018

Project summary

Deep learning has been called the revolutionary technique that quietly

 changed machine vision forever, but is at present mainly applicable to standard RGB images of natural scenes or objects, or otherwise only for other types of imagery when a substantial amount of labelled data is available, which is seldom the case.

This project aims at enabling this technology for computer vision problems anywhere, by developing easy-to-use cognitive solutions also for non-standard images and thereby extending the use of autonomous cognitive computer vision systems to new application areas. The methodology will be general and transferable to other domains such as medical imagery, remote sensing and various industrial applications. Within the project the aim is to solve key big data computer vision challenges in the marine sector.

The overall concept of the project is to exploit the power of deep convolutional neural networks (CNNs) by developing new solutions for learning necessary to classify, localize and segment objects in non-standard, sparsely labelled, image data. Motivated by the methods' ability to generalize and the fact that unlabelled data is often inexpensive to acquire, our approach for solving this will be based on three main concepts; (i) cross-domain transfer learning, (ii) semi-supervised learning and (iii) data augmentation and simulation.

Fisheries and aquaculture are major industries in Norway, and marine image data are acquired in a wide range of formats and modalities for various tasks. Automatic solutions for extracting information from these big non-standard image data will bring exploitation of these data in the marine science to a new level, enabling extraction of new knowledge and continuous monitoring of marine ecosystems. Solutions from the project will also contribute to innovation for industries manufacturing solutions for automated monitoring of fish and marine environments.

Project period

2017 - 2022
Department

    Financing

    Reseach Council of Norway

    Project no. 270966

    Partners

    Institute of Marine Research

    UiT - The Arctic University of Norway

    Scantrol DeepVision AS

    KTH Royal Institute of Technology

    University of Maryland
    Postadresse:
    Norsk Regnesentral
    Postboks 114 Blindern
    0314 Oslo
    Besøksadresse:
    Norsk Regnesentral
    Gaustadalleen 23a
    Kristen Nygaards hus
    0373 Oslo
    Tlf:
    (+47) 22 85 25 00
    Adresse Hvordan komme til NR
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    Personvernerklæring Personvernerklæring
    Postadresse: Norsk Regnesentral, Postboks 114 Blindern, 0314 Oslo
    Besøksadresse: Norsk Regnesentral, Gaustadalleen 23a, Kristen Nygaards hus, 0373 Oslo
    Tlf: (+47) 22 85 25 00
    AdresseHvordan komme til NR