In this paper, we propose an underwater target perception architecture, which adopts the three-stage processing including underwater scene acoustic imaging, local high-order statistics (HOS) space conversion, and region-of-interest (ROI) detection. After analysing the problem of the underwater targets represented by the acoustic images, the unique cube structure of the target in local skewness space is noticed, which is used as a clue to develop the ROI detection of underwater scenes. In order to restore the actual appearance of the ROI as much as possible, the focus processing is explored to achieve the target reconstruction. When the target size and number are unknown, using an uncertain theoretical template can achieve a better target reconstruction effect. The performance of the proposed method in terms of SNR, detection rate, and false alarm rate is verified by experiments with several acoustic image sequences. Moreover, target perception architecture is general and can be generalized to a wider range of underwater applications.

中文翻译：

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本地居屋空间中的水下目标感知

在本文中，我们提出了一种水下目标感知架构，该架构采用水下场景声成像、局部高阶统计（HOS）空间转换和感兴趣区域（ROI）检测三阶段处理。在分析了声学图像所代表的水下目标存在的问题后，注意到了目标在局部偏度空间中独特的立方体结构，以此为线索开展水下场景的ROI检测。为了尽可能还原ROI的实际外观，探索了焦点处理来实现目标重建。当目标大小和数量未知时，使用不确定的理论模板可以达到更好的目标重建效果。所提出方法在信噪比、检测率、并通过多个声学图像序列的实验验证了误报率。此外，目标感知架构是通用的，可以推广到更广泛的水下应用。