FathomNet: An underwater image training database for ocean exploration and discovery

• URL: http://arxiv.org/abs/2007.00114v3
• Date: Fri, 10 Jul 2020 04:14:21 GMT
• Title: FathomNet: An underwater image training database for ocean exploration and discovery
• Authors: Oc\'eane Boulais, Ben Woodward, Brian Schlining, Lonny Lundsten, Kevin Barnard, Katy Croff Bell, and Kakani Katija
• Abstract summary: FathomNet is a novel baseline image training set optimized to accelerate development of modern, intelligent, and automated analysis of underwater imagery. To date, there are more than 80,000 images and 106,000 localizations for 233 different classes, including midwater and benthic organisms. While we find quality results on prediction for this new dataset, our results indicate that we are ultimately in need of a larger data set for ocean exploration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Thousands of hours of marine video data are collected annually from remotely operated vehicles (ROVs) and other underwater assets. However, current manual methods of analysis impede the full utilization of collected data for real time algorithms for ROV and large biodiversity analyses. FathomNet is a novel baseline image training set, optimized to accelerate development of modern, intelligent, and automated analysis of underwater imagery. Our seed data set consists of an expertly annotated and continuously maintained database with more than 26,000 hours of videotape, 6.8 million annotations, and 4,349 terms in the knowledge base. FathomNet leverages this data set by providing imagery, localizations, and class labels of underwater concepts in order to enable machine learning algorithm development. To date, there are more than 80,000 images and 106,000 localizations for 233 different classes, including midwater and benthic organisms. Our experiments consisted of training various deep learning algorithms with approaches to address weakly supervised localization, image labeling, object detection and classification which prove to be promising. While we find quality results on prediction for this new dataset, our results indicate that we are ultimately in need of a larger data set for ocean exploration.

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