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

  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},
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

A memory-based framework for real-time, data-efficient target analysis in forward-looking-sonar (FLS) imagery relies on first removing non-discriminative details from the imagery using a small-scale DENSENET-inspired network, which cascades into a novel NEURALRAM-based convolutional matching network, NRMN, for low-shot target recognition.

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

This guide is intended to aid newcomers in navigating the most critical issues in SA analysis and further supports the development of new theories in the field.



Target Localization in Synthetic Aperture Sonar Imagery using Convolutional Neural Networks

This paper suggests applying a pretrained classification CNN for localizing targets in SAS images and shows the feasibility of target detection and classification in one-step using CNNs.

Exploiting Phase Information in Synthetic Aperture Sonar Images for Target Classification

  • David P. Williams
  • Environmental Science
    2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)
  • 2018
It is demonstrated that the phase information present in complex high-frequency synthetic aperture sonar (SAS) imagery can be exploited for successful object classification. That is, without using

Deep Contrast Learning for Salient Object Detection

  • Guanbin LiYizhou Yu
  • Computer Science, Environmental Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
This paper proposes an end-to-end deep contrast network that significantly improves the state of the art in salient object detection and extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries.

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%.

Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

Expectation-Maximization (EM) methods for semantic image segmentation model training under weakly supervised and semi-supervised settings are developed and extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentsation benchmark, while requiring significantly less annotation effort.

R-FCN: Object Detection via Region-based Fully Convolutional Networks

This work presents region-based, fully convolutional networks for accurate and efficient object detection, and proposes position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.

BASNet: Boundary-Aware Salient Object Detection

Experimental results on six public datasets show that the proposed predict-refine architecture, BASNet, outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures.

Salient Object Detection with Recurrent Fully Convolutional Networks

A new saliency detection method based on recurrent fully convolutional networks (RFCNs) that is able to incorpor- ate saliency prior knowledge for more accurate inference and to automatically learn to refine the saliency map by iteratively correcting its previous errors, yielding more reliable final predictions.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models.

SSD: Single Shot MultiBox Detector

The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.