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  • Abstract: Automated classification of In Situ Ichthyoplankton Imaging System (ISIIS) images using Convolutional Neural Nets on parallel computing infrastructure
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The In Situ Ichthyoplankton Imaging System (ISIIS) is a towed underwater imaging system capable of detecting individual organisms and particles ranging from a few hundred micrometers up to 13 cm. The system has been deployed in near-transparent waters where the image frames (13x13x50 cm) typically yield a few hundred million individual segments of planktonic organisms and detritus particles over the timeline of a project. A pipeline that combines image flat fielding, Region of Interest (ROI) detection and segmentation, as well as a Convolutional Neural Net classifier has been tested and fully implemented in previous projects with excellent results. However, with the interest of understanding the ecology of the highly productive northern California Current Ecosystem, we are facing the challenge of classifying an unprecedented number of images (a two-order of magnitude higher number of images than any previous imagery collection). Doing so with the GPU resources (2 PCI-based k80s and 4 P100s) available to us locally would require years of GPU time. This is likely to become a continuous challenge when studying highly productive waters with high ecological and economic importance. In addition, we are collaborating with an international team of plankton ecologists and computer vision experts to create a standard image-processing pipeline (Belmont Forum Funded Project: World Wide Web of Plankton Images Curation). Hence, we propose to continue advancing the use of Xsede's available computational resources to expedite the automated classification pipeline in order to generate meaningful ecological data, and to address important questions regarding plankton production and distributions in the California Current.

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