mlsanityData to 14 Oct 2026

Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders

arXiv 2101.03603 ↗First posted 10 Jan 2021cs.CV cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 57 of its 172 references that are in the map. First posted on arXiv 10 Jan 2021.

Builds on

57 of 172 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 2.

3 papers.

  1. 2021External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagerycited by 0
  2. 2026A Comparison of Data Augmentation Methods for Training Deep Neural Networks on Synthetic Aperture Sonarcited by 0
  3. 2026LoRA Enhanced Contrastive Learning with SAS Vision Transformerscited by 0