MLMFFNet: Multilevel Mixed Feature Fusion Network for Real-Time Forward-Looking Sonar Image Segmentation

Forward-looking sonar is a critical tool for underwater target detection, and segmentation is an essential component of forward-looking sonar image processing. Accurate segmentation of sonar images is vital, but the complexity of the underwater environment introduces challenges, such as low resolution, significant noise, and blurred target edges. These factors make real-time, precise segmentation particularly difficult. To address these challenges, we propose a novel real-time segmentation network, the multilevel mixed feature fusion network (MLMFFNet), specifically designed for forward-looking sonar images. Our approach leverages a unique MFF module and a multiscale MFF module to extract both local and contextual information using deep convolutional networks, dilated convolutions, and partial convolution combinations for effective information integration. Additionally, we incorporate a context connection module to enhance feature fusion by utilizing high-level contextual information. To further improve accuracy, we introduce three weighted loss functions designed to address imbalanced sample distributions and blurred boundaries. Experimental evaluations on two distinct forward-looking sonar data sets demonstrate that MLMFFNet significantly outperforms many state-of-the-art general and sonar-specific semantic segmentation networks, delivering superior segmentation accuracy while maintaining real-time performance.