  In this paper, we present the first large-scale dataset for semantic
Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with
pixel annotations for eight object categories: fish (vertebrates), reefs
(invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and
sea-floor. The images have been rigorously collected during oceanic
explorations and human-robot collaborative experiments, and annotated by human
participants. We also present a benchmark evaluation of state-of-the-art
semantic segmentation approaches based on standard performance metrics. In
addition, we present SUIM-Net, a fully-convolutional encoder-decoder model that
balances the trade-off between performance and computational efficiency. It
offers competitive performance while ensuring fast end-to-end inference, which
is essential for its use in the autonomy pipeline of visually-guided underwater
robots. In particular, we demonstrate its usability benefits for visual
servoing, saliency prediction, and detailed scene understanding. With a variety
of use cases, the proposed model and benchmark dataset open up promising
opportunities for future research in underwater robot vision.
