Shale Rosen

Deep Vision: An In-Trawl Stereo Camera Makes a Step Forward in Monitoring the Pelagic Community

Ecosystem surveys are carried out annually in the Barents Sea by Russia and Norway to monitor the spatial distribution of ecosystem components and to study population dynamics. One component of the survey is mapping the upper pelagic zone using a trawl towed at several depths. However, the current technique with a single codend does not provide fine-scale spatial data needed to directly study species overlaps. An in-trawl camera system, Deep Vision, was mounted in front of the codend in order to acquire continuous images of all organisms passing. It was possible to identify and quantify of most young-of-the-year fish (e.g. Gadus morhua, Boreogadus saida and Reinhardtius hippoglossoides) and zooplankton, including Ctenophora, which are usually damaged in the codend. The system showed potential for measuring the length of small organisms and also recorded the vertical and horizontal positions where individuals were imaged. Young-of-the-year fish were difficult to identify when passing the camera at maximum range and to quantify during high densities. In addition, a large number of fish with damaged opercula were observed passing the Deep Vision camera during heaving; suggesting individuals had become entangled in meshes farther forward in the trawl. This indicates that unknown numbers of fish are probably lost in forward sections of the trawl and that the heaving procedure may influence the number of fish entering the codend, with implications for abundance indices and understanding population dynamics. This study suggests modifications to the Deep Vision and the trawl to increase our understanding of the population dynamics.

Species-specific residence times in the aft part of a pelagic survey trawl: implications for inference of pre-capture spatial distribution using the Deep Vision system

Species-specific residence times in the aft part of a pelagic survey trawl: implications for inference of pre-capture spatial distribution using the Deep Vision system Melanie J. Underwood*, Shale Rosen, Arill Engås, Terje Jørgensen, and Anders Fernö Department of Biology, University of Bergen, PO Box 7800, NO-5020 Bergen, Norway Institute of Marine Research, PO Box 1870, Nordnes, NO-5817 Bergen, Norway *Corresponding author: tel: þ47 941 27 085; e-mail: melanie.underwood@imr.no.

Fish species identification using a convolutional neural network trained on synthetic data

&NA; Acoustic‐trawl surveys are an important tool for marine stock management and environmental monitoring of marine life. Correctly assigning the acoustic signal to species or species groups is a challenge, and recently trawl camera systems have been developed to support interpretation of acoustic data. Examining images from known positions in the trawl track provides high resolution ground truth for the presence of species. Here, we develop and deploy a deep learning neural network to automate the classification of species present in images from the Deep Vision trawl camera system. To remedy the scarcity of training data, we developed a novel training regime based on realistic simulation of Deep Vision images. We achieved a classification accuracy of 94% for blue whiting, Atlantic herring, and Atlantic mackerel, showing that automatic species classification is a viable and efficient approach, and further that using synthetic data can effectively mitigate the all too common lack of training data.