Efficient convolutional neural networks (CNNs) are designed and trained for an underwater target classification task with synthetic aperture sonar (SAS) imagery collected at sea. The main contribution is demonstrating that classification performance that matches, and even surpasses, the level achievable by a human domain expert obtained from tiny CNNs with three to six orders of magnitude fewer parameters than have traditionally been used in the literature. In doing so, this work represents the first large-scale classification study in the sonar domain to establish a favorable comparison between automated algorithm performance and human ability. Extensive experimental results on challenging real-world SAS image data sets collected in diverse environments and conditions demonstrate that the CNNs possess strong generalization ability. These findings should significantly impact the manner in which CNNs are utilized in the underwater remote-sensing community. To wit, the tiny CNNs proposed here provide a blueprint for achieving excellent classification performance even with limited computing power or limited data.

中文翻译：

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在声纳图像中使用微型卷积神经网络进行人类专家级分类性能

高效的卷积神经网络 (CNN) 是针对水下目标分类任务而设计和训练的，该任务具有在海上收集的合成孔径声纳 (SAS) 图像。主要贡献是证明分类性能匹配甚至超过人类领域专家从微型 CNN 获得的水平，其参数比文献中传统使用的参数少三到六个数量级。在这样做时，这项工作代表了声纳领域的第一次大规模分类研究，以建立自动化算法性能和人类能力之间的有利比较。在不同环境和条件下收集的具有挑战性的现实世界 SAS 图像数据集的大量实验结果表明，CNN 具有很强的泛化能力。这些发现应该会显着影响 CNN 在水下遥感社区中的使用方式。也就是说，这里提出的微型 CNN 提供了一个蓝图，即使在计算能力或数据有限的情况下也能实现出色的分类性能。