Coral Identification and Counting with an Autonomous Underwater Vehicle

MD Modasshir, Sharmin Rahman, Oscar Youngquist, Ioannis Rekleitis
In IEEE International Conference on Robotics and Biomimetics (ROBIO) 2018.

Abstract

Abstract: 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\hyp held camera validate the proposed approach for different image qualities.

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BibTeX

@inproceedings{ModasshirRobio2018,
  author       = {MD Modasshir and Sharmin Rahman and Oscar Youngquist and
		 Ioannis Rekleitis},
  title        = {{Coral Identification and Counting with an Autonomous
		 Underwater Vehicle}},
  booktitle    = {IEEE International Conference on Robotics and Biomimetics
		 (ROBIO)},
  year	       = {2018},
  pages        = {524-- 529},
  month        = {Dec.},
  address      = {Kuala Lumpur, Malaysia, (Finalist of T. J. Tarn Best
		 Paper in Robotics)}
}

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Mon Sep 16 06:21:02 EDT 2019