A novel sonar image target detection model based on fast large kernel attention network

As artificial intelligence technology continues to advance, the utilization of deep learning in sonar image target detection is growing. This trend has garnered significant interest in maritime defense infrastructure and marine economic development as a promising research avenue. Nevertheless, existing sonar image target detection methods face challenges due to the inherent characteristics of sonar images, such as low resolution, blurred edges, and significant speckle noise. These issues often result in large model parameters and slow inference speeds. To mitigate these challenges, this study introduces a novel approach: a sonar image target detection model utilizing a Fast Large Kernel Attention Network (FLKANet). The FLKANet enhances feature extraction capabilities, reduces model parameters, and improves inference speed. Moreover, employing Large Separable Kernel Attention (LSKA) instead of Small Patch Feature Fusion (SPFF) enables the model to prioritize the shape of sonar targets over texture, thereby enhancing detection accuracy and inference speed. Experimental results on the SCTD sonar image dataset demonstrate the superiority of the proposed model in terms of parameter count, computational complexity, inference speed, and detection accuracy. These findings offer valuable insights for deploying edge-side models in future applications.