Coral Identification and Counting with an Autonomous Underwater Vehicle
| Autor: | Ioannis Rekleitis, Modasshir, Oscar Youngquist, Sharmin Rahman |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
geography
education.field_of_study geography.geographical_feature_category 010504 meteorology & atmospheric sciences Coral Real-time computing Population 02 engineering and technology Coral reef 01 natural sciences Convolutional neural network Visualization Identification (information) Counting problem 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Underwater education 0105 earth and related environmental sciences |
| Zdroj: | ROBIO |
| DOI: | 10.1109/robio.2018.8664785 |
| Popis: | 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 AUV s. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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