Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders

@article{Sledge2021TargetDA,
  title={Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders},
  author={Isaac J. Sledge and Matthew S. Emigh and Jonathan Lee King and Denton L. Woods and James Tory Cobb and Jos{\'e} Carlos Pr{\'i}ncipe},
  journal={IEEE Journal of Oceanic Engineering},
  year={2021},
  volume={47},
  pages={1099-1128}
}
In this article, we propose a framework for saliency-based multitarget detection and segmentation of circular-scan synthetic aperture sonar (CSAS) imagery. Our framework relies on a multibranch 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… 

External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery

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