ISBN 1574-9541
This paper focuses on the detection of Posidonia oceanica in underwater images. The input image is split into a set of patches that are classified as depicting Posidonia
or not. Two different Neural Networks are proposed to perform the
classification. A region growing algorithm able to accurately detect the
contours of the Posidonia oceanica from the output of the classifier is also described.
The
experimental results, performed using images gathered in coastal areas
of Mallorca (Spain), show that our proposal reaches the 95.5% of
accuracy, the 95.8% of precision and the 95.4% of recall with only a
4.6% of fallout. When compared to previous studies, these results
surpass those of methods based on Machine Learning and are comparable,
even superior in some cases, to recent Deep Learning approaches. The
advantages in terms of computational requirements, which are crucial in
underwater robotics, are also highlighted. In particular, our proposal
works up to 8.6 times faster than other Deep Learning methods.
