Path-Routing Convolution and Scalable Lightweight Networks for Robust Underwater Acoustic Target Recognition

Maritime traffic surveillance and ocean environmental protection urgently require the accurate identification of surface vessel types. Although deep learning methods have significantly improved the underwater acoustic target recognition performance, the existing models suffer from large parameter counts and fail to adapt to the multi-scale spectral features of radiated noise from different vessel types, restricting their practical deployment on power-constrained underwater sensors. To address these challenges, this paper proposes a novel path-routing convolution mechanism that achieves the discriminative extraction of cross-scale acoustic features through multi-dilation-rate parallel paths and an adaptive routing strategy and designs the MobilePR-ConvNet unified architecture that enables a single framework to automatically adapt to diverse hardware platforms through systematic width scaling. Experiments on the DeepShip and ShipsEar datasets demonstrate that the proposed method achieved 98.58% and 97.82% recognition accuracies, respectively, while maintaining a 77.8% robust performance under 10 dB low-signal-to-noise-ratio conditions, validating the cross-dataset generalization capability in complex marine environments and providing an effective solution for intelligent deployment on resource-constrained underwater devices.