HFDNet: High-Frequency Divergence Attention Network for Underwater Segmentation

Abstract

Currently, most underwater operations are conducted in deep water, and there is usually insufficient illumination in these areas. At this time, the local texture features of some objects are highly similar in images, and it is difficult to distinguish the inter-class boundaries. This typically results in poor performance of the current semantic segmentation models of terrestrial images in underwater scenes. Taking advantage of the general characteristic that high-frequency regions are more likely to correspond to semantic segmentation boundaries, we introduce the high-frequency Divergence Attention Network (HFDNet), a semantic segmentation model based on transformer. HFDNet extracts its frequency distribution by analyzing the frequency domain of the feature map, and then calculates the relative spectral magnitude of each component by comparing its frequency amplitude against the average amplitude within its local neighborhood in the frequency domain. The local frequency map can be incorporated into the attention matrix as a weighting factor to realize the divergence of attention to the surrounding areas, which improves the attention to the high-frequency areas. This operation can enhance the model’s focus on the object boundary region and local neigh-borhood categories for each component. Therefore, our model can alleviate the problem of determining the object boundary caused by insufficient light in underwater image segmentation, and enhance the ability to segment objects with similar local features under low light conditions. We conduct comprehensive experiments on three underwater segmentation datasets: Caveseg, SUIM and UWS. The results show that our HFDNet achieves state-of-the-art (SOTA) performance on the testing datasets. The source code is available at https://github.com/cv516Buaa/HongboXie/tree/main/HFDNet.