An Attention Nested U-Structure Suitable for Salient Ship Detection in Complex Maritime Environment

Salient ship detection plays an important role in ensuring the safety of maritime transportation and navigation. However, due to the influence of waves, special weather, and illumination on the sea, existing saliency methods are still unable to achieve effective ship detection in a complex marine environment. To solve the problem, this paper proposed a novel saliency method based on an attention nested U-Structure (AU2Net). First, to make up for the shortcomings of the U-shaped structure, the pyramid pooling module (PPM) and global guidance paths (GGPs) are designed to guide the restoration of feature information. Then, the attention modules are added to the nested U-shaped structure to further refine the target characteristics. Ultimately, multi-level features and global context features are integrated through the feature aggregation module (FAM) to improve the ability to locate targets. Experiment results demonstrate that the proposed method could have at most 36.75% improvement in F-measure (Favg) compared to the other state-of-the-art methods.