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Automated Analysis of Underwater Imagery: Accomplishments, Products, and Vision
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2019
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[PDF-2.70 MB]
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Alternative Title:A Report on the NOAA Fisheries Strategic Initiative on Automated Image Analysis 2014–2018
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Description:Stock assessments, as required by the Magnuson-Stevens Fishery Conservation and Management Act, are the cornerstone of U.S. marine resource management. However, inadequate abundance data remains an impediment. Increasingly, fisheries surveys are conducted using imaging systems that allow for efficient and non-lethal collection of voluminous data to fill this gap. However, the generated image data volumes exceed human analysis capacity. Automated image processing methods exist, but are nascent within the marine science community. The NOAA Fisheries Office of Science and Technology initiated a Strategic Initiative (SI) on Automated Image Analysis with the goal of creating an open-source software toolkit allowing for automated analysis of optical data streams to provide fishery-independent abundance estimates for use in stock assessment. The SI was directed by a research board comprising representatives from each of the NOAA Fisheries Science Centers (SC) as well as academic and private industry partners. Over the course of its five-year term, the SI developed two main products, the Video and Image Analytics for a Marine Environment (VIAME) open-source software toolkit and the CoralNet web-based solution for benthic image analysis. CoralNet has become the operational image analysis tool for the Pacific Islands Fisheries Science Center (PIFSC) Coral Reef Ecosystem Program (CREP), accounting for more than 1 million annotations comprising more than 100,000 images. VIAME has been released on GitHub as an open-source, publicly available software tool. Computing hardware has been procured and training sessions have been conducted at each NOAA Fisheries Science Center. VIAME is currently being used within the analysis workflow for (1) CamTrawl— AFSC Walleye Pollock assessment; (2) HabCam—NEFSC Scallop assessment; and (3) MOUSS—PIFSC Deep7 Bottomfish assessment. Although VIAME is primarily used for underwater imagery, it is based on a generic, pipelined, deep learning-based processing system that applies to any domain. VIAME includes a graphical user interface (GUI) and modeling capabilities for users to create new automated analytics, interactively without any programming, enabling direct applicability to other NOAA imaging domains such as protected species (e.g. marine mammals, turtles), plankton, and electronic monitoring. Efforts are underway to raise awareness of VIAME and nascent collaborations exist within these domains. VIAME and CoralNet exceeded expectations and continue to grow with increased utility spanning a broad range of programs. With major development complete, support for ongoing maintenance and customer support is needed to ensure continued utility and to support project-specific development. To maximize development imagery should be curated with a priority on access for machine learning.
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Rights Information:Public Domain
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Compliance:Submitted
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