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Creators/Authors contains: "Youngquist, Oscar"

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  1. ; ; ; ; ; ; (, New Frontiers in Summarization workshop (at EMNLP 2021))
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  2. ; ; ; (, 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO))
    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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