# Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows identification

Source: [Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows identification](<https://www.sota2.com/research/paper/68fa65551e209131cea09c38>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:24:52.728Z

Authors: Miguel Martin-Abadal, Eric Guerrero-Font, Francisco Bonin-Font, Yolanda Gonzalez-Cid

Published: 2018-06-22T22:21:51.000Z

## Abstract

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 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 labelling the images manually. Also, the network is implemented in an Autonomous Underwater Vehicle (AUV), performing an online P.O. segmentation, which will be used to generate real-time semantic coverage maps.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.03117>)

[PDF](<https://arxiv.org/pdf/1807.03117.pdf>)