Abstract—The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef
organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists
to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We
demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape
survey. In the second deployment, we demonstrate CUREE’s ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.
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Coral Identification and Counting with an Autonomous Underwater Vehicle
Monitoring coral reef populations as part of
environmental assessment is essential. Recently, many marine
science researchers are employing low-cost and power efficient
Autonomous Underwater Vehicles (AUV) to survey coral reefs.
While the counting problem, in general, has rich literature, little
work has focused on estimating the density of coral population
using AUVs. This paper proposes a novel approach to identify,
count, and estimate coral populations. A Convolutional Neural
Network (CNN) is utilized to detect and identify the different
corals, and a tracking mechanism provides a total count for
each coral species per transect. Experimental results from
an Aqua2 underwater robot and a stereo hand-held camera
validated the proposed approach for different image qualities.
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- Award ID(s):
- 1659514
- NSF-PAR ID:
- 10166774
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)
- Page Range / eLocation ID:
- 524 to 529
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ROBIO.2018.8664785
