Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows Identification

Authors Miguel Martín Abadal | Eric Guerrero Font | Francisco Bonin-Font | Yolanda González Cid
In IEEE Access, 2018.
ISBN 2169-3536

Recent studies have shown evidence of a significant decline of the Posidonia oceanica (P.O.) meadows on a global scale. The monitoring and mapping of these meadows are fundamental tools for measuring their status. We present an approach based on a deep neural network to automatically perform a high precision semantic segmentation of the P.O. meadows in sea-floor images, offering several improvements over the state-of-the-art techniques. Our network demonstrates outstanding performance over diverse test sets, reaching a precision of 96.57% and an accuracy of 96.81%, surpassing the reliability of labeling the images manually. Moreover, the network is implemented in an autonomous underwater vehicle, performing an online P.O. segmentation, which will be used to generate real-time semantic coverage maps.


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