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Presenter: Vaneeda Shalini Devi Allken, Institute of Marine Research.
Abstract: Fish counts and species information can be obtained from images taken within trawls, which enables trawl surveys to operate without extracting fish from their habitat, yields distribution data at fine scale for better interpretation of acoustic results, and can detect fish that are not retained in the catch due to mesh selection. To automate the process of image-based fish detection and identification, we trained a deep learning algorithm (RetinaNet) on images collected from the trawl-mounted DeepVision camera system. We focus on the detection of blue whiting, Atlantic herring, Atlantic mackerel, and mesopelagic fishes from images collected in the Norwegian sea. To address the need for large amounts of annotated data to train these models, we used a combination of real and synthetic images. Regression models were used to compare predicted fish counts, which were derived from RetinaNet classification of fish in the individual image frames, with catch data collected at 20 trawl stations. With our model, we can automatically detect and count fish from individual trawl images. This method will be used in future trawl surveys.
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