Ship wake segmentation network based on attention mechanism

Aiming at the problem that traditional deep learning semantic segmentation networks are difficult to achieve high-precision segmentation of ship wakes, a VGG-UNet semantic segmentation network model based on the channel prior convolutional attention (CPCA) was proposed. First, a ship wake dataset of optical remote sensing images was constructed through a literature review and visual interpretation, and the dataset was expanded through data augmentation techniques. Second, the U-Net network architecture was improved by incorporating a visual geometry group (VGG) backbone for feature extraction in the encoder section, integrating the CPCA into the skip connection part of the VGG-UNet model, and adopting transfer learning strategies to enhance feature acquisition capabilities. Finally, the improved VGG-UNet was trained, and comparison experiments and ablation experiments were conducted. The experimental results demonstrate that the improved network model achieves 87.68%, 92.67%, and 91.58% on the three evaluation metrics of mean intersection over union I_m, mean recall R_m and mean pixel accuracy P_m, respectively, and all its evaluation metrics are superior to those of the U-Net, VGG-UNet, and Res-UNet network models. The proposed network model exhibits higher segmentation accuracy, providing a new approach for more complete segmentation of ship wakes.