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

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.

@article{Miguel2018ART, 
author={M. Martin-Abadal and E. Guerrero-Font and F. Bonin-Font and Y. Gonzalez-Cid}, 
journal={IEEE Access}, 
title={Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows Identification}, 
year={2018}, 
volume={6}, 
number={}, 
pages={60956-60967}, 
keywords={Semantics;Training;Image segmentation;Neural networks;Computer architecture;Decoding;Cameras;Deep learning;online semantic segmentation;Posidonia oceanica;autonomous underwater vehicle}, 
doi={10.1109/ACCESS.2018.2875412}, 
ISSN={2169-3536}, 
month={},}