Hybrid Mamba-CNN network for forward-looking sonar image segmentation with acoustic background suppression mechanism

Forward-Looking Sonar (FLS) is a critical tool for underwater surveying, resource exploration, and deep-sea mapping. Recent CNN-Transformer hybrid frameworks have demonstrated strong performance on FLS segmentation by jointly capturing fine-grained local details and long-range dependencies. Nevertheless, their reliance on self-attention mechanisms incurs substantial computational overhead, limiting their efficiency and scalability. To address these limitations, we propose MambaSonar, an efficient CNN-Mamba model for FLS image segmentation. MambaSonar combines the local feature extraction strengths of convolutional neural networks (CNN) with Mamba's efficient global dependency modeling via its selective state-space mechanism. To better adapt Mamba to the characteristics of FLS images, we introduce an acoustic background suppression block, which reduces environmental noise and emphasizes meaningful target responses, and a Mamba-CNN fusion block, which bridges semantic gaps and effectively integrates multi-scale features. Extensive experiments on several public FLS datasets demonstrate that MambaSonar delivers superior segmentation accuracy while maintaining high computational efficiency. In addition, ablation studies also validate the effectiveness of the proposed model and its components, highlighting the potential of hybrid CNN-Mamba architectures for challenging underwater imaging tasks.