Existing sonar target detection methods suffer from low efficiency and accuracy due to sparse target features and significant noise interference in sonar images. To address this, we introduce SFE-YOLO, an improved model based on YOLOv11. We replace the original detection head with an FSAFFHead module that enables adaptive spatial feature fusion. An EEA module is designed to direct the model’s attention to the intrinsic contour information of targets. We also enhance SC_Conv convolution and integrate it into C3K2 to improve detection stability and reduce information redundancy. Additionally, Focaler-IOU is introduced to boost the accuracy of multi-category target bounding box regression. Lastly, we employ a hybrid training strategy that combines pre-training with ADA-StyleGAN3-generated data and transfer learning with real data to alleviate the problem of insufficient training samples. The experiments show that, compared to the baseline YOLOv11n, the improved model’s precision and recall increase to 92% and 90.3%, respectively, and mAP50 rises by 12.7 percentage points, highlighting the effectiveness of the SFE-YOLO network and its transfer learning strategy in tackling the challenges of sparse small target features and strong noise interference in sonar images.

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.