This paper proposes a method to perform on-line multi-class segmentation of Side-Scan Sonar acoustic images, thus being able to build a semantic map of the sea bottom usable to search loop candidates in a SLAM context. The proposal follows three main steps. First, the sonar data is pre-processed by means of acoustics based models. Second, the data is segmented thanks to a lightweight Convolutional Neural Network which is fed with acoustic swaths gathered within a temporal window. Third, the segmented swaths are fused into a consistent segmented image. The experiments, performed with real data gathered in coastal areas of Mallorca (Spain), explore all the possible configurations and show the validity of our proposal both in terms of segmentation quality, with per-class precisions and recalls surpassing the 90%, and in terms of computational speed, requiring less than a 7% of CPU time on a standard laptop computer. The fully documented source code, and some trained models and datasets are provided as part of this study.

中文翻译：

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使用自主水下航行器的侧扫声纳图像的在线多类分割

本文提出了一种对Side-Scan Sonar声图像进行在线多类分割的方法，从而能够建立海底的语义图，可用于在SLAM上下文中搜索循环候选。该提案遵循三个主要步骤。首先，借助基于声学的模型对声纳数据进行预处理。其次，由于使用了轻量级的卷积神经网络，对数据进行了分段，该神经网络被馈入了在时间窗口内收集的声幅。第三，将分割的条带融合成一致的分割图像。实验是利用在西班牙马洛卡沿海地区收集的真实数据进行的，探索了所有可能的配置，并在分割质量，每类精度和召回率均超过90％以及在计算速度方面 在标准笔记本电脑上所需的CPU时间少于7％。这项研究的一部分提供了完整记录的源代码以及一些训练有素的模型和数据集。