Ship-radiated noise (SRN) recognition is vital for underwater acoustics, with applications in both military and civilian fields. Traditional manual recognition by sonar operators is inefficient and error-prone, motivating the development of automated recognition systems. However, most existing deep learning approaches demand high computational resources, limiting their deployment on resource-constrained edge devices. To overcome this challenge, we propose LW-MS-LFTFNet, a lightweight model informed by time-frequency analysis of SRN that highlights the critical role of low-frequency components. The network integrates a multi-scale depthwise separable convolutional backbone with CBAM attention for efficient spectral representation, along with two LSTM-based modules to capture temporal dependencies in low-frequency bands. Experiments on the DeepShip dataset show that LW-MS-LFTFNet achieves 75.04% accuracy with only 0.85 M parameters, 0.38 GMACs, and 3.27 MB of storage, outperforming representative lightweight architectures. Ablation studies further confirm that low-frequency temporal modules contribute complementary gains, improving accuracy by 2.64% with minimal overhead. Guided by domain-specific priors derived from time-frequency pattern analysis, LW-MS-LFTFNet achieves efficient and accurate SRN recognition with strong potential for edge deployment.

Forward-looking sonar (FLS) target detection is essential for autonomous underwater vehicles (AUVs), yet its effectiveness is severely hindered by complex acoustic distortions, environmental volatility and the scarcity of fine-annotated data, which limit the success of standard deep learning approaches. To address these challenges, this study proposes a novel quasi-static in situ learning paradigm for underwater acoustic target detection (UATD). The hybrid methodology integrates scene priors into a lightweight deep learning detector by incorporating explicit probability weighting based on echo-intensity statistics and acoustic attenuation compensation. By using these models for pixel-wise image enhancement and fusing statistical descriptors with deep learning predictions at the score level, the framework dynamically adapts to in situ environmental contexts during quasi-static operational tranches. Experimental evaluations on the UATD dataset demonstrate that this in situ adaptation significantly enhances overall detection performance, achieving an F1-score of 0.865 for our approach, an 8.1% improvement over the baseline YOLOv12n, with only a 2.1 G increase in FLOPs, while outperforming YOLOv12x (F1 = 0.844) with 95% fewer FLOPs. Ultimately, this paradigm overcomes the limitations of purely deep learning-based methods, offering a robust and interpretable solution tailored for practical AUV deployment.