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  • 1
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    MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration (2018)
    Public Library of Science
    In:  PLoS ONE, 13 (11). e0207498.
    Details
    Publication Date: 2021-02-08
    Description: Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to “traditional” annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections.
    Type: Article , PeerReviewed
    Format: text
    Format: archive
    DOI: 10.1371/journal.pone.0207498
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 2
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    BIIGLE 2.0 - Browsing and Annotating Large Marine Image Collections (2017)
    Frontiers
    In:  Frontiers in Marine Science, 4 (83).
    Details
    Publication Date: 2020-11-23
    Description: Combining state-of-the art digital imaging technology with different kinds of marine exploration techniques such as modern AUV (autonomous underwater vehicle), ROV (remote operating vehicle) or other monitoring platforms enables marine imaging on new spatial and/or temporal scales. A comprehensive interpretation of such image collections requires the detection, classification and quantification of objects of interest in the images usually performed by domain experts. However, the data volume and the rich content of the images makes the support by software tools inevitable. We define some requirements for marine image annotation and present our new online tool Biigle 2.0. It is developed with a special focus on annotating benthic fauna in marine image collections with tools customized to increase efficiency and effectiveness in the manual annotation process. The software architecture of the system is described and the special features of Biigle 2.0 are illustrated with different use-cases and future developments are discussed.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    Format: text
    DOI: 10.3389/fmars.2017.00083
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 3
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    Rapid image processing and classification in underwater exploration using advanced high performance computing (2015)
    Institute of Electrical and Electronics Engineers (IEEE)
    In:  In: OCEANS 2015. Institute of Electrical and Electronics Engineers (IEEE), Washington, pp. 1-5. ISBN 978-0-933957-43-5
    Details
    Publication Date: 2016-12-01
    Description: Computational underwater image analysis is developing into a mature field of research, with an increasing number of companies, academic groups and researchers showing interest in it. While on the one hand, the basic question is addressed by many groups, how algorithms can be applied to automatically detect and classify objects of interest (OOI) in underwater image footage, on the other hand the questions for efficiency and performance, i.e. the time a computer (or a compute cluster) needs to perform this task, has received much attention yet. In this paper we will show, how nowadays methods for high performance computing like parallelization and GPU computing via CUDA (Compute Unified Device Architecture) can be used to achieve both, image enhancement and segmentation in less than 0:2 sec per image (4224 x 2376 pixel) on average, which paves the way to real time online applications.
    Type: Book chapter , NonPeerReviewed
    Format: text
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    OceanRep: Book chapter
  • 4
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    Megafauna community assessment of polymetallic nodule fields with cameras: Platform and methodology comparison (2020)
    Copernicus Publications (EGU)
    Details
    Publication Date: 2023-02-08
    Description: With the mining of polymetallic nodules from the deep-sea seafloor once more evoking commercial interest, decisions must be taken on how to most efficiently regulate and monitor physical and community disturbance in these remote ecosystems. Image-based approaches allow non-destructive assessment of the abundance of larger fauna to be derived from survey data, with repeat surveys of areas possible to allow time series data collection. At the time of writing, key underwater imaging platforms commonly used to map seafloor fauna abundances are autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs) and towed camera “ocean floor observation systems” (OFOSs). These systems are highly customisable, with cameras, illumination sources and deployment protocols changing rapidly, even during a survey cruise. In this study, eight image datasets were collected from a discrete area of polymetallic-nodule-rich seafloor by an AUV and several OFOSs deployed at various altitudes above the seafloor. A fauna identification catalogue was used by five annotators to estimate the abundances of 20 fauna categories from the different datasets. Results show that, for many categories of megafauna, differences in image resolution greatly influenced the estimations of fauna abundance determined by the annotators. This is an important finding for the development of future monitoring legislation for these areas. When and if commercial exploitation of these marine resources commences, robust and verifiable standards which incorporate developing technological advances in camera-based monitoring surveys should be key to developing appropriate management regulations for these regions.
    Type: Article , PeerReviewed
    Format: text
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    On the impact of Citizen Science-derived data quality on deep learning based classification in marine images (2019)
    Public Library of Science
    In:  PLOS ONE, 14 (6). e0218086.
    Details
    Publication Date: 2022-01-31
    Description: The evaluation of large amounts of digital image data is of growing importance for biology, including for the exploration and monitoring of marine habitats. However, only a tiny percentage of the image data collected is evaluated by marine biologists who manually interpret and annotate the image contents, which can be slow and laborious. In order to overcome the bottleneck in image annotation, two strategies are increasingly proposed: “citizen science” and “machine learning”. In this study, we investigated how the combination of citizen science, to detect objects, and machine learning, to classify megafauna, could be used to automate annotation of underwater images. For this purpose, multiple large data sets of citizen science annotations with different degrees of common errors and inaccuracies observed in citizen science data were simulated by modifying “gold standard” annotations done by an experienced marine biologist. The parameters of the simulation were determined on the basis of two citizen science experiments. It allowed us to analyze the relationship between the outcome of a citizen science study and the quality of the classifications of a deep learning megafauna classifier. The results show great potential for combining citizen science with machine learning, provided that the participants are informed precisely about the annotation protocol. Inaccuracies in the position of the annotation had the most substantial influence on the classification accuracy, whereas the size of the marking and false positive detections had a smaller influence.
    Type: Article , PeerReviewed
    Format: text
    DOI: 10.1371/journal.pone.0218086
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 6
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    Report on the Marine Imaging Workshop 2022 (2024)
    Pensoft
    Details
    Publication Date: 2024-03-25
    Description: Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4 th Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower-income countries, early career scientists, all working on laboratory, benthic and pelagic imaging. This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.
    Type: Article , NonPeerReviewed
    Format: text
    Format: archive
    Location Call Number Limitation Availability
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    OceanRep: Article in a Scientific Journal - without review
  • 7
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    Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification (2020)
    In:  EPIC3Frontiers in Marine Science, 7, ISSN: 2296-7745
    Details
    Publication Date: 2021-01-26
    Description: In marine research, image data sets from the same area but collected at different times allow seafloor fauna communities to be monitored over time. However, ongoing technological developments have led to the use of different imaging systems and deployment strategies. Thus, instances of the same class exhibit slightly shifted visual features in images taken at slightly different locations or with different gear. These shifts are referred to as concept drift in the domains computational image analysis and machine learning as this phenomenon poses particular challenges for these fields. In this paper, we analyse four different data sets from an area in the Peru Basin and show how changes in imaging parameters affect the classification of 12 megafauna morphotypes with a 34-layer ResNet. Images were captured using the ocean floor observation system, a traditional sled-based system, or an autonomous underwater vehicle, which is used as an imaging platform capable of surveying larger regions. ResNet applied on separate individual data sets, i.e., without concept drift, showed that changing object distance was less important than the amount of training data. The results for the image data acquired with the ocean floor observation system showed higher performance values than data collected with the autonomous underwater vehicle. The results from this concept drift studies indicate that collecting image data from many dives with slightly different gear may result in training data well-suited for learning taxonomic classification tasks and that data volume can compensate for light concept drift.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
    Location Call Number Limitation Availability
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    PAPER (OA)
  • 8
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    Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison (2020)
    In:  EPIC3Biogeosciences, 17(12), pp. 3115-3133, ISSN: 1726-4189
    Details
    Publication Date: 2021-01-26
    Description: With the mining of polymetallic nodules from the deep-sea seafloor once more evoking commercial interest, decisions must be taken on how to most efficiently regulate and monitor physical and community disturbance in these remote ecosystems. Image-based approaches allow non-destructive assessment of the abundance of larger fauna to be derived from survey data, with repeat surveys of areas possible to allow time series data collection. At the time of writing, key underwater imaging platforms commonly used to map seafloor fauna abundances are autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs) and towed camera “ocean floor observation systems” (OFOSs). These systems are highly customisable, with cameras, illumination sources and deployment protocols changing rapidly, even during a survey cruise. In this study, eight image datasets were collected from a discrete area of polymetallic-nodule-rich seafloor by an AUV and several OFOSs deployed at various altitudes above the seafloor. A fauna identification catalogue was used by five annotators to estimate the abundances of 20 fauna categories from the different datasets. Results show that, for many categories of megafauna, differences in image resolution greatly influenced the estimations of fauna abundance determined by the annotators. This is an important finding for the development of future monitoring legislation for these areas. When and if commercial exploitation of these marine resources commences, robust and verifiable standards which incorporate developing technological advances in camera-based monitoring surveys should be key to developing appropriate management regulations for these regions.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
    Location Call Number Limitation Availability
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    PAPER (OA)
  • 9
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    The Quest for Seafloor Macrolitter: A Critical Review of Background Knowledge, Current Methods and Future Prospects (2021)
    IOP PUBLISHING LTD
    In:  EPIC3Environmental Research Letters, IOP PUBLISHING LTD, 16(2), pp. 1-29, ISSN: 1748-9326
    Details
    Publication Date: 2021-01-20
    Description: The seafloor covers some 70% of the Earth's surface and has been recognised as a major sink for marine litter. Still, litter on the seafloor is the least investigated fraction of marine litter, which is not surprising as most of it lies in the deep sea, i.e. the least explored ecosystem. Although marine litter is considered a major threat for the oceans, monitoring frameworks are still being set up. This paper reviews current knowledge and methods, identifies existing needs, and points to future developments that are required to address the estimation of seafloor macrolitter. It provides background knowledge and conveys the views and thoughts of scientific experts on seafloor marine litter offering a review of monitoring and ocean modelling techniques. Knowledge gaps that need to be tackled, data needs for modelling, and data comparability and harmonisation are also discussed. In addition, it shows how research on seafloor macrolitter can inform international protection and conservation frameworks to prioritise efforts and measures against marine litter and its deleterious impacts.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 10
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    Megafauna community assessment with cameras: Platform, annotator and methodology comparison (2020)
    In:  EPIC3Ocean Science Meeting, San Diego, California, USA
    Details
    Publication Date: 2021-03-25
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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