Sonar-based object detection for autonomous underwater vehicles in marine environments

Sonar image object detection plays a crucial role in obstacle detection, target recognition, and environmental perception in autonomous underwater vehicles (AUVs). However, the complex underwater acoustic environment introduces various interferences, such as noise, scattering, and echo, which hinder the effectiveness of existing object detection methods in achieving satisfactory accuracy and robustness. To address these challenges in forward-looking sonar (FLS) images, we propose a novel multi-level feature aggregation network (MLFANet). Specifically, to mitigate the impact of seabed reverberation noise, we designed a low-level feature aggregation module (LFAM), which enhances key low-level image features, such as texture, edges, and contours in the object regions. Given the common presence of shadow interference in sonar images, we introduce the discriminative feature extraction module (DFEM) to suppress redundant features in the shadow regions and emphasize the object region features. To tackle the issue of object scale variation, we designed a multi-scale feature refinement module (MFRM) to improve both classification accuracy and positional precision by refining the feature representations of objects at different scales. Additionally, the CIoU-DFL loss optimization function was constructed to address the class imbalance in sonar data and reduce model computational complexity. Extensive experimental results demonstrate that our method outperforms state-of-the-art detectors on the Underwater Acoustic Target Detection (UATD) dataset. Specifically, our approach achieves a mean average precision (mAP) of 81.86%, an improvement of 7.85% compared to the best-performing existing model. These results highlight the superior performance of our method in marine environments.