  In this work, we investigate a Deep Learning (DL) approach to fish
segmentation in a small dataset of noisy low-resolution images generated by a
forward-looking multibeam echosounder (MBES). We build on recent advances in DL
and Convolutional Neural Networks (CNNs) for semantic segmentation and
demonstrate an end-to-end approach for a fish/non-fish probability prediction
for all range-azimuth positions projected by an imaging sonar. We use
self-collected datasets from the Danish Sound and the Faroe Islands to train
and test our model and present techniques to obtain satisfying performance and
generalization even with a low-volume dataset. We show that our model proves
the desired performance and has learned to harness the importance of semantic
context and take this into account to separate noise and non-targets from real
targets. Furthermore, we present techniques to deploy models on low-cost
embedded platforms to obtain higher performance fit for edge environments -
where compute and power are restricted by size/cost - for testing and
prototyping.
