# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
###### Abstract  
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-JOU 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. s 2025 1  
In recent years, significant breakthroughs have been achieved in addressing the technical challenges of underwater acoustic target detection through systematic academic research. The [PERSON] team [3] developed an innovative methodology employing Support Vector Machines (SVM) with an integrated highlight-shadow detection framework, advancing feature interpretation in synthetic aperture sonar (SAS) imagery. The Lopera Research Group [4] proposed a cascade-based solution for identifying man-made linear objects (MLO) on seabeds through multi-scale feature fusion: implementing joint noise suppression and edge enhancement via anisotropic diffusion filtering, coupled with precise highlight/shadow segmentation using fuzzy morphological operators. At the feature engineering level, they innovatively constructed a 32-dimensional hybrid feature vector incorporating morphological topology parameters, regional statistical characteristics, contour moment descriptors, and minimum enclosing ellipse features. These were integrated with a Bayesian probabilistic classification model using the Markov Chain Monte Carlo (MCMC) methods, demonstrating substantial improvement in average classification accuracy across both field-collected and simulated datasets. [PERSON] et al. [5] pioneered the application of Independent Component Analysis (ICA) to side-scan sonar (SSS) target detection, establishing a feature decoupling mechanism based on blind source separation that effectively suppresses seabed reverberation interference. Their framework achieved a 77% recall rate for automatic identification of small-scale targets (diameter < 0.5 m) in complex seabed sediment environments. In the field of sonar image target detection, traditional computer vision methodologies based on feature-classifier coupling mechanisms historically dominated technological evolution. However, as marine observation scenarios grow increasingly heterogeneous (e.g., mixed seabed substrates and dynamic hydroacoustic environmental perturbations), the inherent constraints of manual feature engineering--particularly in target representation capacity and generalization performance--have become critically exposed. A pivotal transition occurred in 2012 when AlexNet achieved breakthrough performance on the ImageNet large-scale visual recognition challenge through its deep convolutional neural network (CNN) architecture, propelling a paradigm shift in underwater optical/acoustic image interpretation via deep learning.