FathomNet: An underwater image training database for ocean exploration and discovery
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...
Gespeichert in:
Hauptverfasser: | , , , , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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. |
---|---|
DOI: | 10.48550/arxiv.2007.00114 |