Underwater acoustic target recognition (UATR) is usually difficult due to the complex and multipath underwater environment. Currently, deep-learning (DL)-based UATR methods have proved their effectiveness and have outperformed the traditional methods by using powerful convolution neural networks (CNNs) to extract discriminative features on acoustic spectrograms. However, CNNs always fail to capture the global information implicated in the spectrogram due to the use of a small kernel and thus encounter the performance bottleneck. To this end, we propose the UATR-transformer based on a convolution-free architecture, referred to as the transformer, which can perceive both the global and local information from acoustic spectrograms, and thus improve the accuracy. Experiments on two real-world data demonstrate that our proposed model has achieved comparative results to the state of art CNNs and thus can be applied to certain cases in UATR.

中文翻译：

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用于水下声学目标识别的基于 Transformer 的深度学习网络

由于水下环境复杂且多路径，水下声学目标识别（UATR）通常很困难。目前，基于深度学习 (DL) 的 UATR 方法已经证明了它们的有效性，并且通过使用强大的卷积神经网络 (CNN) 在声谱图上提取判别特征，其性能优于传统方法。然而，由于使用了小内核，CNN 总是无法捕捉到频谱图中涉及的全局信息，从而遇到性能瓶颈。为此，我们提出了基于无卷积架构的UATR-transformer，称为transformer，它可以从声谱图中感知全局和局部信息，从而提高精度。