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

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 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 and compare it with 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.