  We propose a framework for saliency-based, multi-target detection and
segmentation of circular-scan, synthetic-aperture-sonar (CSAS) imagery. Our
framework relies on a multi-branch, convolutional encoder-decoder network
(MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features
from CSAS images. These features are fed into dual decoders that perform
pixel-level segmentation to mask targets. Each decoder provides different
perspectives as to what constitutes a salient target. These opinions are
aggregated and cascaded into a deep-parsing network to refine the segmentation.
  We evaluate our framework using real-world CSAS imagery consisting of five
broad target classes. We compare against existing approaches from the
computer-vision literature. We show that our framework outperforms supervised,
deep-saliency networks designed for natural imagery. It greatly outperforms
unsupervised saliency approaches developed for natural imagery. This
illustrates that natural-image-based models may need to be altered to be
effective for this imaging-sonar modality.
