Deep Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data

The fully supervised approaches achieve good performance provided that high-quality training data and an appropriate choice for the prediction model are assured. However, it is highly challenging for the echosounder data to obtain the class annotation for each backscattering intensity pixel because this relies on the manual annotation process, which is expensive and error-prone.
As a key step in this direction, we propose a novel deep semi-supervised semantic segmentation method that efficient- ly uses a small amount of manually annotated data by com- bining it with a large amount of readily available unannotated data in the learning process.

Poster presentation at VI days 2023. The original work is published in IEEE Journal of Oceanic Engineering DOI: 10.1109/JOE.2022.3226214.