Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows identification
This provides an automated solution for environmental scientists and conservationists to monitor and map endangered seagrass meadows, though it is incremental as it builds on existing deep learning techniques for segmentation.
The paper tackles the problem of monitoring declining Posidonia oceanica meadows by developing a deep neural network for high-precision semantic segmentation in sea-floor images, achieving 96.57% precision and 96.81% accuracy, and implementing it in an AUV for real-time mapping.
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.