Efficient and Interpretable Underwater Acoustic Target Recognition Using a Lightweight Heterogeneous Kernel Network

Abstract

Underwater acoustic target recognition (UATR) is challenging due to the complex, multi-scale physical characteristics of marine targets and the strict computational limits of edge platforms like unmanned surface vehicles. To navigate the severe interference of underwater environments, existing methods increasingly rely on heavyweight architectures to achieve high recognition accuracy. However, the massive computational overhead of these models is fundamentally at odds with the restricted power and processing capabilities of practical deployment platforms. To resolve this conflict between performance and deployability, we propose LHK-Net, a lightweight Heterogeneous Kernel Network. By integrating a Heterogeneous Kernel Pyramid with Residual Depthwise Separable Convolutions, LHK-Net dynamically captures multi-scale acoustic features, from macroscopic steady-state harmonics to localized transient impulses, while compressing the model size to merely 0.82 M parameters. Additionally, a dual-domain Time–Frequency Attention module and an Adaptive SK-Fusion mechanism are incorporated for robust noise suppression. Experiments on the DeepShip dataset demonstrate that LHK-Net achieves state-of-the-art accuracy, outperforming heavyweight models at real-time speeds. Extensive visual analyses further validate that the network possesses strong physical interpretability, effectively aligning its internal feature representations with the intrinsic acoustic properties of the targets.