Underwater acoustic source localization is formulated as a feature learning problem within a machine learning framework, where a data-driven approach directly extracts source distance features from hydroacoustic signals. This study systematically compares the localization performance of four machine learning models—decision tree (DT), random forest (RF), support vector machine (SVM), and feedforward neural network (FNN) models—in both classification and regression tasks. Experimental results demonstrate that, in classification tasks, all algorithms achieve effective localization under high signal-to-noise ratio (SNR) conditions, while the DT model exhibits significant noise sensitivity in low-SNR scenarios; regression tasks show reduced model convergence overall, with only the SVM and RF models maintaining basic localization capabilities at a high SNR. For two-dimensional localization, machine learning classification algorithms are employed, revealing systematic accuracy degradation compared to one-dimensional scenarios, where only the RF and SVM models demonstrate practical value under high-SNR conditions. Validation using measured data from the SWellEx-96 experiment’s S5 event confirms that when constructing datasets with frequency-domain acoustic pressure features from the final 35 min segment, the classification task-driven DT, RF, and SVM models all demonstrate reliable localization performance, benefiting from the inherent high-SNR characteristics of the data.

Underwater object detection is a core technology for environmental perception and autonomous operation of unmanned underwater vehicles (UUVs). However, optical and acoustic sensing alone suffer from physical limitations, leading to missed and false detections in turbid, low-light, or long-range conditions. To overcome these limitations, this paper develops an acoustic–optical multimodal fusion detection module (AOMFDM) tailored for UUV deployment. The module employs dual YOLOv5 models for separate processing of sonar and optical images. An interference source quantification estimation network is introduced to extract environmental degradation features, including noise, blur, illumination, contrast, and color cast. A heterogeneous feature map matching network and a deep sparse autoencoder are further designed to achieve cross-modal alignment and fusion of acoustic and optical features. Additionally, attention mechanisms, anchor-based box annotation, and weighted boxes fusion (WBF) are incorporated to enhance detection robustness. For model training and evaluation, we construct the Underwater Sonar Detection (USD) and Underwater Optical Detection (UOD) datasets, covering diverse water qualities, illumination levels, target materials, and interference scenarios. Experimental results demonstrate that, by exploiting the complementarity of acoustic and optical modalities together with adaptive alignment strategies, the proposed module significantly boosts both detection reliability and generalization capability for UUVs in challenging underwater environments.

In this paper, a modal decomposition method is employed to analyze the properties of Rayleigh–Lamb waves propagating in an isotropic plate containing an open surface-breaking crack. The mode conversion coefficients (MCCs) are calculated as functions of crack depth and ultrasonic frequency for both an incident Lamb wave. Compared with the incident A0 mode, the MCC of the S0 mode exhibits a less smooth dependence on the phase of the crack modulated. The experimental, finite element method (FEM) and analytical results confirm that crack characteristics can be quantitatively evaluated from the Lamb wave mode conversion indued by the crack. This finding is beneficial for interpreting the detected ultrasonic signals in scenarios involving crack measurements between different waves, such as the modulation of the probing wave’s MCC by the material response in a tensile test machine.